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.gitignore
vendored
7
.gitignore
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@ -45,10 +45,3 @@ docs/
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# translation temp files
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po/*~
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# Customs
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*.RTF
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*.png
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*.html
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*.zip
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*.pdf
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*.csv
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197
00 master.R
197
00 master.R
@ -1,197 +0,0 @@
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## =============================================================================
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## Notes
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## =============================================================================
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# Regression analysis relies on the script biv_mul_man.R and two functions: function_reg_table.R and function_trans_table.R
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#
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# Tables are exported as csv-files. gtsummary has been tried, but performance and flexibility was not op to the task.
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# Dependencies has been minimised to primarily use base-R and a tiny bit of dplyr.
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#
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# Focus has been on universal functions, and the amount of flags has accumulated...
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#
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# Only retransformation is performed on independent variables (these are not marked in the exported tables).
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# Coefs and CIs are transformed according to this:
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# https://stats.stackexchange.com/questions/93089/reporting-regression-statistics-after-logarithmic-transformation to allow for interpretation.
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## =============================================================================
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## Table 1
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## =============================================================================
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source("data_format.R")
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source("table_1.R")
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tbl_1
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## =============================================================================
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## Primary regression analysis
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## =============================================================================
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source("data_format.R")
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outs<-c("mdi_1_enr","mdi_6_newobs_enr")
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lbl_x <- NULL # Extra label in file name
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inter_reg <- NULL # Interaction variables to include (only multivariate)
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biv_mul <- TRUE # Sets flag for both bivariate and multivariate or only multivariate analysis
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strat_var <- NULL # Variable to stratify by. Only one variable(!)
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trans_vars <- TRUE # Transform variables? T/F
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sqrt_vars<-"pase_0" # Variables to sqrt-transfom
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log1p_vars<-"nihss_0" # Variables to log1p-transform, not outcome
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log_vars <- NULL # Variables to log-transform
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log1p_vars_all<-c(log1p_vars,outs) # All variables to log1p-transform, incl outcome
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trans_back <- TRUE # Back transform variables? T/F
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print_tbl <- TRUE # Print tables? T/F
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# source("biv_mul.R")
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source("biv_mul_man.R")
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export
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# bm_16_tbl
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strat_var <- "active_treat" # Variable to stratify by. Only one variable(!)
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# source("biv_mul.R")
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source("biv_mul_man.R")
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export
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# bm_16_tbl
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## =============================================================================
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## Sensitivity regression analysis
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## =============================================================================
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source("data_format.R")
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outs<-c("mdi_1","mdi_6")
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lbl_x <- "_sens" # Extra label in file name
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inter_reg <- NULL # Interaction variables to include (only multivariate)
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biv_mul <- TRUE # Sets flag for both bivariate and multivariate or only multivariate analysis
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strat_var <- NULL # Variable to stratify by. Only one variable(!)
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trans_vars <- TRUE # Transform variables? T/F
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sqrt_vars<-"pase_0" # Variables to sqrt-transfom
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log1p_vars<-"nihss_0" # Variables to log1p-transform, not outcome
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log_vars <- NULL # Variables to log-transform
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log1p_vars_all<-c(log1p_vars,outs) # All variables to log1p-transform, incl outcome
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trans_back <- TRUE # Back transform variables? T/F
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print_tbl <- TRUE # Print tables? T/F
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# source("biv_mul.R")
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source("biv_mul_man.R")
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# rbind("One month",bm_list[[1]][-1],"Six month",bm_list[[2]][-1])
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# bm_16_tbl
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strat_var <- "active_treat" # Variable to stratify by. Only one variable(!)
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# source("biv_mul.R")
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source("biv_mul_man.R")
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# bm_16_tbl
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## =============================================================================
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## Interaction regression analysis
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## =============================================================================
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source("data_format.R")
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outs<-c("mdi_1_enr","mdi_6_newobs_enr")
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lbl_x <- "_inter" # Extra label in file name
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inter_reg <- c("active_treat","pase_0") # Interaction variables to include (only multivariate)
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biv_mul <- TRUE # Sets flag for both bivariate and multivariate or only multivariate analysis
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trans_vars <- TRUE # Transform variables? T/F
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strat_var <- NULL # Variable to stratify by. Only one variable(!)
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sqrt_vars<-"pase_0" # Variables to sqrt-transfom
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log1p_vars<-"nihss_0" # Variables to log1p-transform, not outcome
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log_vars <- NULL # Variables to log-transform
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log1p_vars_all<-c(log1p_vars,outs) # All variables to log1p-transform, incl outcome
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trans_back <- TRUE # Back transform variables? T/F
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print_tbl <- TRUE # Print tables? T/F
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# source("regression_interaction.R")
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source("biv_mul_man.R")
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export
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## =============================================================================
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## NIHSS~PASE0 regression analysis
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## =============================================================================
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source("data_format.R")
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vars<-c("pase_0", # New variables for analysis
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"female",
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"age",
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"cohab",
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"ever_smoker",
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"diabetes",
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"hypertension",
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"afli",
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"ami",
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"tci",
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"pad")
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outs<-c("nihss_0")
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lbl_x <- "_nihss-pase" # Extra label in file name
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inter_reg <- NULL # Interaction variables to include (only multivariate)
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|
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biv_mul <- FALSE # Sets flag for both bivariate and multivariate or only multivariate analysis
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trans_vars <- TRUE # Transform variables? T/F
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strat_var <- NULL # Variable to stratify by. Only one variable(!)
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sqrt_vars<-"pase_0" # Variables to sqrt-transfom
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log1p_vars<-NULL # Variables to log1p-transform, not outcome
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log_vars <- NULL # Variables to log-transform
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log1p_vars_all<-c(outs) # All variables to log1p-transform, incl outcome
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trans_back <- TRUE # Back transform variables? T/F
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print_tbl <- TRUE # Print tables? T/F
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# source("regression_nihss-pase.R")
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source("biv_mul_man.R")
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export
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BIN
Archive/.DS_Store
vendored
BIN
Archive/.DS_Store
vendored
Binary file not shown.
@ -1,70 +0,0 @@
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## =============================================================================
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## Requirements
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## =============================================================================
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library(gtsummary)
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## =============================================================================
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## Loop
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## =============================================================================
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bm_list_strat<-list()
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for (i in 1:length(outs)){
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## Bivariate
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biv<-dta |>
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dplyr::select(all_of(c("active_treat",vars,outs[i]))) |>
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tbl_strata(
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strata = active_treat,
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.tbl_fun =
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~ .x %>%
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tbl_uvregression(data=.,
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y=outs[i],
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method=lm,
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label = lab_sel(labels_all,vars)
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)|>
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add_global_p()|>
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bold_p() |>
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bold_labels() |>
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italicize_levels(),
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.header = "**{strata}**, N = {n}"
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)
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## Multivariate
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mul<-dta |>
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dplyr::select(all_of(c("active_treat",vars,outs[i]))) |>
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tbl_strata(
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strata = active_treat,
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.tbl_fun =
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~ .x %>%
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lm(formula(paste(c(outs[i],"."),collapse="~")),
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data = .) |>
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tbl_regression(label = lab_sel(labels_all,vars)
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)|>
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add_n() |>
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add_global_p() |>
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bold_p() |>
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bold_labels() |>
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italicize_levels(),
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.header = "**{strata}**, N = {n}"
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)
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## Merge
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biv_mul_strat<-tbl_merge(
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tbls = list(biv, mul),
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tab_spanner = c("**Bivariate linear regression**",
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"**Multivariate linear regression**")
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)
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bm_list_strat[[i]]<-biv_mul_strat
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}
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## =============================================================================
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## Big merge
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## =============================================================================
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bm_16_tbl_strat<-tbl_merge(
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tbls = bm_list_strat,
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tab_spanner = c("**One month follow up**",
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"**Six months follow up**")
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)
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@ -1,35 +0,0 @@
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## Tests and visualisation
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source("data_format.R")
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## Box-Cox power transformation performs comparably to logarithmic transformation. The latter is much easier to explain.
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library(MASS)
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dta_bc<-dta_backup|>
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dplyr::select(all_of(c("mdi_6_newobs_enr",vars)))|>
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mutate(pase_0=sqrt(pase_0),
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mdi_6_newobs_enr=mdi_6_newobs_enr+1)#|>
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# na.omit()
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bc<-boxcox(mdi_6_newobs_enr~.,data=dta_bc)
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lambda <- bc$x[which.max(bc$y)]
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## Q-Q plots to compare the two different approaches, and the non-transformed
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q1 <- qqnorm(lm(((mdi_6_newobs_enr^lambda-1)/lambda) ~ .,data=dta_bc)$residuals)
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q2 <- qqnorm(lm(log(mdi_6_newobs_enr) ~ .,data=dta_bc)$residuals)
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library(patchwork)
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plot(q1); plot(q2)
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## Histograms for reference
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h1 <- hist(dta_bc$pase_0,40); hist(sqrt(dta_bc$pase_0),40)
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h2 <- hist(log(dta_bc$mdi_6_newobs_enr),40); hist((dta_bc$mdi_6_newobs_enr),40) ## Observed MDI, and log transformed MDI
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plot(h1); plot(h2)
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@ -1,507 +0,0 @@
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---
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title: "revised statistics"
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author: "AGDamsbo"
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date: "8/1/2022"
|
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output: html_document
|
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toc: true
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---
|
||||
|
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```{r setup, include=FALSE}
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knitr::opts_chunk$set(echo = TRUE)
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```
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|
||||
```{r}
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library(REDCapR)
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library(gtsummary)
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theme_gtsummary_compact()
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library(REDCapR)
|
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library(gt)
|
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|
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library(lubridate)
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library(dplyr)
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library(tidyr)
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|
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```
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# Import
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||||
|
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```{r}
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dta_all<-read.csv("/Volumes/Data/depression/dep_dataset.csv")
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```
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# Defining patients to include for analysis
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Only including cases with complete pase_0 and MDI at 1 & 6 months
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```{r}
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dta<-dta_all[!is.na(dta_all$pase_0),]
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# &!is.na(dta$mdi_1)&!is.na(dta$mdi_6)
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```
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## Formatting
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```{r echo=FALSE}
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dta$diabetes<-factor(dta$diabetes)
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dta$pad<-factor(dta$pad)
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|
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dta$cohab<-ifelse(dta$civil=="partner","yes","no")|>
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factor()
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|
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dta$hypertension<-factor(dta$hypertension)
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|
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dta$afli[dta$afli=="unknown"]<-NA
|
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dta$afli<-factor(dta$afli)
|
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|
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dta$ever_smoker<-ifelse(dta$smoke_ever=="ever","yes","no")|>
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factor()
|
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|
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dta$ami<-factor(dta$ami)
|
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dta$tci<-factor(dta$tci)
|
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dta$thrombolysis<-factor(dta$thrombolysis)
|
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dta$thrombechtomy<-factor(dta$thrombechtomy)
|
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|
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dta$any_reperf<-ifelse(dta$rep_any=="rep","yes","no")|>
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factor()
|
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|
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dta$pad<-factor(dta$pad)
|
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dta$nihss_0<-as.numeric(dta$nihss_0)
|
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dta$age<-as.numeric(dta$age)
|
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|
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dta$active_treat<-ifelse(dta$rtreat=="Active","yes","no")|>
|
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factor()
|
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# dta$rtreat<-factor(dta$rtreat)
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|
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dta$female<-ifelse(dta$sex=="female","yes","no")|>
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factor()
|
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|
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dta$pase_0<-as.numeric(dta$pase_0)
|
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dta$pase_6<-as.numeric(dta$pase_6)
|
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dta$bmi<-as.numeric(dta$bmi)
|
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dta$mdi_6<-as.numeric(dta$mdi_6)
|
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dta$pase_0_bin<-factor(dta$pase_0_bin,levels=c("lower","higher"))
|
||||
|
||||
dta$nihss_0_isna<-is.na(dta$nihss_0)
|
||||
```
|
||||
|
||||
```{r}
|
||||
vars<-c("pase_0",
|
||||
"female",
|
||||
"age",
|
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"cohab",
|
||||
"ever_smoker",
|
||||
"diabetes",
|
||||
"hypertension",
|
||||
"afli",
|
||||
"ami",
|
||||
"tci",
|
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"pad",
|
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"nihss_0",
|
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"any_reperf")
|
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|
||||
# tbl1_vars<-c("thrombolysis", "thrombechtomy","inc_time")
|
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|
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labels_all<-list(active_treat~"Active trial treatment",
|
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pase_0~"PASE score",
|
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age~"Age",
|
||||
female~"Female sex",
|
||||
ever_smoker~"History of smoking",
|
||||
cohab~"Cohabitation",
|
||||
diabetes~"Known diabetes",
|
||||
hypertension~"Known hypertension",
|
||||
afli~"Known Atrialfibrillation",
|
||||
ami~"Previos myocardial infarction",
|
||||
tci~"Previos TIA",
|
||||
pad~"Known peripheral arteriosclerotic disease",
|
||||
nihss_0~"NIHSS score",
|
||||
thrombolysis~"Thrombolytic therapy",
|
||||
thrombechtomy~"Endovascular treatment",
|
||||
any_reperf~"Any reperfusion treatment",
|
||||
inc_time~"Study inclusion time",
|
||||
'[Intercept]'~"Intercept")
|
||||
|
||||
lab_sel<-function(label_list,variables_vector){
|
||||
## Helper function to select labels for gtsummary function from list of all labels based on selected variables.
|
||||
## Long names in try to ease reading.
|
||||
include_index<-c()
|
||||
for (i in 1:length(label_list)) {
|
||||
include_index[i]<-as.character(label_list[[i]])[2] %in% variables_vector
|
||||
}
|
||||
return(label_list[include_index])
|
||||
}
|
||||
|
||||
dta_backup<-dta
|
||||
```
|
||||
|
||||
# Table 1
|
||||
```{r}
|
||||
tbl1_vars<-c("active_treat",vars,"inc_time")
|
||||
|
||||
dta|>
|
||||
tbl_summary(missing = "ifany",
|
||||
include = all_of(tbl1_vars),
|
||||
missing_text="(Missing)",
|
||||
label = lab_sel(labels_all,tbl1_vars)
|
||||
)|>
|
||||
add_n()|>
|
||||
as_gt() |>
|
||||
# modify with gt functions
|
||||
gt::tab_header("Baseline Characteristics") |>
|
||||
gt::tab_options(
|
||||
table.font.size = "small",
|
||||
data_row.padding = gt::px(1))
|
||||
```
|
||||
|
||||
|
||||
# Regression - all
|
||||
|
||||
```{r}
|
||||
outs<-c("mdi_1_enr","mdi_6_newobs_enr")
|
||||
```
|
||||
|
||||
## Non-stratified
|
||||
|
||||
```{r}
|
||||
source("biv_mul.R")
|
||||
|
||||
bm_16_tbl_rtf <- file("bm_16_tbl.RTF", "w")
|
||||
writeLines(bm_16_tbl%>%as_gt()%>%as_rtf(), bm_16_tbl_rtf)
|
||||
close(bm_16_tbl_rtf)
|
||||
|
||||
bm_16_tbl %>% # build gtsummary table
|
||||
as_gt() %>% # convert to gt table
|
||||
gt::gtsave( # save table as image
|
||||
filename = "bm_16_tbl.png"
|
||||
)
|
||||
```
|
||||
|
||||
|
||||
## Stratified by treatment
|
||||
|
||||
```{r}
|
||||
source("biv_mul_strat.R")
|
||||
|
||||
bm_16_tbl_strat_rtf <- file("bm_16_tbl_strat.RTF", "w")
|
||||
writeLines(bm_16_tbl_strat%>%as_gt()%>%as_rtf(), bm_16_tbl_strat_rtf)
|
||||
close(bm_16_tbl_strat_rtf)
|
||||
|
||||
bm_16_tbl_strat %>% # build gtsummary table
|
||||
as_gt() %>% # convert to gt table
|
||||
gt::gtsave( # save table as image
|
||||
filename = "bm_16_tbl_strat.png"
|
||||
)
|
||||
```
|
||||
|
||||
# Regression - sensitivity
|
||||
|
||||
```{r}
|
||||
outs<-c("mdi_1","mdi_6")
|
||||
```
|
||||
|
||||
## Non-stratified
|
||||
|
||||
```{r}
|
||||
source("biv_mul.R")
|
||||
|
||||
bm_16_sens_tbl_rtf <- file("bm_16_sens_tbl.RTF", "w")
|
||||
writeLines(bm_16_tbl%>%as_gt()%>%as_rtf(), bm_16_sens_tbl_rtf)
|
||||
close(bm_16_sens_tbl_rtf)
|
||||
|
||||
bm_16_tbl %>% # build gtsummary table
|
||||
as_gt() %>% # convert to gt table
|
||||
gt::gtsave( # save table as image
|
||||
filename = "bm_16_tbl_sens.png"
|
||||
)
|
||||
```
|
||||
|
||||
|
||||
## Stratified by treatment
|
||||
|
||||
```{r}
|
||||
source("biv_mul_strat.R")
|
||||
|
||||
bm_16_tbl_strat_sens_rtf <- file("bm_16_tbl_strat_sens.RTF", "w")
|
||||
writeLines(bm_16_tbl_strat%>%as_gt()%>%as_rtf(), bm_16_tbl_strat_sens_rtf)
|
||||
close(bm_16_tbl_strat_sens_rtf)
|
||||
|
||||
bm_16_tbl_strat %>% # build gtsummary table
|
||||
as_gt() %>% # convert to gt table
|
||||
gt::gtsave( # save table as image
|
||||
filename = "bm_16_tbl_strat_sens.png"
|
||||
)
|
||||
```
|
||||
|
||||
|
||||
# Regression - interaction
|
||||
|
||||
```{r}
|
||||
outs<-c("mdi_1_enr","mdi_6_newobs_enr")
|
||||
```
|
||||
|
||||
```{r}
|
||||
## =============================================================================
|
||||
## Loop
|
||||
## =============================================================================
|
||||
|
||||
bm_list<-list()
|
||||
|
||||
for (i in 1:length(outs)){
|
||||
## Multivariate
|
||||
mul<-dta |>
|
||||
dplyr::select(all_of(c("active_treat",vars,outs[i])))|>
|
||||
lm(formula(
|
||||
paste(c(paste(c(outs[i],paste(c("active_treat","pase_0"),collapse="*")),collapse="~"),"."),collapse="+")),
|
||||
data = _)|>
|
||||
tbl_regression(label = lab_sel(labels_all,c(vars,"active_treat")))|>
|
||||
add_n() |>
|
||||
add_global_p() |>
|
||||
bold_p() |>
|
||||
bold_labels() |>
|
||||
italicize_levels()
|
||||
|
||||
bm_list[[i]]<-mul
|
||||
}
|
||||
|
||||
## =============================================================================
|
||||
## Big merge
|
||||
## =============================================================================
|
||||
|
||||
tbl_merge(
|
||||
tbls = bm_list,
|
||||
tab_spanner = c("**One month follow up**",
|
||||
"**Six months follow up**")
|
||||
)
|
||||
```
|
||||
|
||||
```{r}
|
||||
library(aod)
|
||||
|
||||
for (i in 1:length(outs)){
|
||||
model<-dta |>
|
||||
dplyr::select(all_of(c("active_treat",vars,outs[i])))|>
|
||||
lm(formula(
|
||||
paste(c(paste(c(outs[i],paste(c("active_treat","pase_0"),collapse="*")),collapse="~"),"."),collapse="+")),
|
||||
data = _)
|
||||
|
||||
wt<-wald.test(Sigma = vcov(model),
|
||||
b = coef(model),
|
||||
Terms = model$rank # Rank gives number of coefficients. The interaction is the last.
|
||||
)
|
||||
print(wt)
|
||||
}
|
||||
```
|
||||
|
||||
# Regression - nihss ~ pase_0
|
||||
|
||||
```{r}
|
||||
vars_nihss<-c(
|
||||
"pase_0",
|
||||
"female",
|
||||
"age",
|
||||
"cohab",
|
||||
"ever_smoker",
|
||||
"diabetes",
|
||||
"hypertension",
|
||||
"afli",
|
||||
"ami",
|
||||
"tci",
|
||||
"pad",
|
||||
"nihss_0")
|
||||
|
||||
## Bivariate
|
||||
biv<-dta|>
|
||||
dplyr::select(all_of(vars_nihss))|>
|
||||
tbl_uvregression(data=_,
|
||||
y="nihss_0",
|
||||
method=lm#,
|
||||
#label = lab_sel(labels_all,vars)
|
||||
) |>
|
||||
add_global_p()|>
|
||||
bold_p() |>
|
||||
bold_labels() |>
|
||||
italicize_levels()
|
||||
|
||||
## Multivariate
|
||||
mul<-dta |>
|
||||
dplyr::select(all_of(vars_nihss))|>
|
||||
lm(nihss_0~pase_0+.,
|
||||
data = _) |>
|
||||
tbl_regression(
|
||||
#label = lab_sel(labels_all,vars),
|
||||
#intercept=FALSE
|
||||
)|>
|
||||
add_n() |>
|
||||
add_global_p() |>
|
||||
bold_p() |>
|
||||
bold_labels() |>
|
||||
italicize_levels()
|
||||
|
||||
## Merge
|
||||
tbl_merge(
|
||||
tbls = list(biv, mul),
|
||||
tab_spanner = c("**Bivariate linear regression**",
|
||||
"**Multivariate linear regression**")
|
||||
)
|
||||
|
||||
|
||||
```
|
||||
|
||||
|
||||
|
||||
# Transformed data
|
||||
```{r}
|
||||
|
||||
# dta<-dta_backup
|
||||
dta<-dta|>
|
||||
mutate(pase_0=sqrt(pase_0),
|
||||
nihss_0=log1p(nihss_0),
|
||||
mdi_1_enr=log1p(mdi_1_enr), # log1p(x) svarer til log(x+1)
|
||||
mdi_6_newobs_enr=log1p(mdi_6_newobs_enr))|>
|
||||
data.frame()
|
||||
|
||||
library(dplyr)
|
||||
# source("biv_mul.R")
|
||||
|
||||
```
|
||||
|
||||
```{r}
|
||||
## =============================================================================
|
||||
## Loop
|
||||
## =============================================================================
|
||||
|
||||
bm_list<-list()
|
||||
|
||||
|
||||
|
||||
for (i in 1:length(outs)){
|
||||
## Bivariate
|
||||
|
||||
dta_l<-dta|>
|
||||
dplyr::select(all_of(c("active_treat",vars,outs[i])))
|
||||
|
||||
sel<-dta_l|>
|
||||
sapply(is.factor)
|
||||
|
||||
# i=1
|
||||
biv<-dta_l|>
|
||||
tbl_uvregression(data=_,
|
||||
y=outs[i],
|
||||
method=lm,
|
||||
label = lab_sel(labels_all,vars),
|
||||
show_single_row=colnames(dta_l)[sel]
|
||||
) |>
|
||||
add_global_p()|>
|
||||
bold_p() |>
|
||||
bold_labels() |>
|
||||
italicize_levels()
|
||||
|
||||
## What follows is the pragmatic transformation and reformatting
|
||||
|
||||
## Transforming log2() to 2^()
|
||||
# biv$table_body$estimate<-expm1(biv$table_body$estimate)
|
||||
# biv$table_body$conf.low<-expm1(biv$table_body$conf.low)
|
||||
# biv$table_body$conf.high<-expm1(biv$table_body$conf.high)
|
||||
#
|
||||
# ## Transforming sqrt(pase_0) to pase_0^2
|
||||
# biv$table_body$estimate[biv$table_body$variable=="pase_0"]<-
|
||||
# biv$table_body$estimate[biv$table_body$variable=="pase_0"]^2
|
||||
#
|
||||
# low<-biv$table_body$conf.low[biv$table_body$variable=="pase_0"]^2
|
||||
# high<-biv$table_body$conf.high[biv$table_body$variable=="pase_0"]^2
|
||||
#
|
||||
# biv$table_body$conf.low[biv$table_body$variable=="pase_0"]<-high
|
||||
# biv$table_body$conf.high[biv$table_body$variable=="pase_0"]<-low
|
||||
#
|
||||
# ## New confidence intervals
|
||||
# # biv$table_body$estimate<-format(biv$table_body$estimate, drop0trailing = F,digits =2)
|
||||
# biv$table_body$ci<-paste(formatC(biv$table_body$conf.low, digits = 3, format = "f"),
|
||||
# formatC(biv$table_body$conf.high, digits = 3, format = "f"),
|
||||
# sep=", ")
|
||||
|
||||
# biv$table_body$ci<-paste(format(biv$table_body$conf.low, digits = 2, drop0trailing = FALSE),
|
||||
# format(biv$table_body$conf.high, digits = 2, drop0trailing = FALSE),
|
||||
# sep=", ")
|
||||
|
||||
# Formatting from this: https://stackoverflow.com/questions/12243071/r-keeping-0-0-when-using-paste-or-paste0
|
||||
|
||||
## multivariate
|
||||
mul<-dta_l |>
|
||||
lm(formula(paste(c(outs[i],"."),collapse="~")),
|
||||
data = _) |>
|
||||
tbl_regression(label = lab_sel(labels_all,vars),
|
||||
show_single_row=colnames(dta_l)[sel]
|
||||
)|>
|
||||
add_n() |>
|
||||
add_global_p() |>
|
||||
bold_p() |>
|
||||
bold_labels() |>
|
||||
italicize_levels()
|
||||
|
||||
## Merge
|
||||
biv_mul<-tbl_merge(
|
||||
tbls = list(biv, mul),
|
||||
tab_spanner = c("**Bivariate linear regression**",
|
||||
"**Multivariate linear regression**")
|
||||
)
|
||||
|
||||
bm_list[[i]]<-biv_mul
|
||||
}
|
||||
|
||||
## =============================================================================
|
||||
## Big merge
|
||||
## =============================================================================
|
||||
|
||||
bm_16_tbl<-tbl_merge(
|
||||
tbls = bm_list,
|
||||
tab_spanner = c("**One month follow up**",
|
||||
"**Six months follow up**")
|
||||
)
|
||||
bm_16_tbl
|
||||
```
|
||||
|
||||
|
||||
```{r}
|
||||
bm_16_tbl_rtf <- file("bm_16_tbl_trans.RTF", "w")
|
||||
writeLines(bm_16_tbl%>%as_gt()%>%as_rtf(), bm_16_tbl_rtf)
|
||||
close(bm_16_tbl_rtf)
|
||||
|
||||
bm_16_tbl %>% # build gtsummary table
|
||||
as_gt() %>% # convert to gt table
|
||||
gt::gtsave( # save table as image
|
||||
filename = "bm_16_tbl_trans.png"
|
||||
)
|
||||
```
|
||||
|
||||
## Tests and visualisation
|
||||
|
||||
Box-Cox power transformation performs comparably to logarithmic transformation. The latter is much easier to explain.
|
||||
|
||||
```{r}
|
||||
library(MASS)
|
||||
dta_bc<-dta_backup|>
|
||||
dplyr::select(all_of(c("mdi_6_newobs_enr",vars)))|>
|
||||
mutate(pase_0=sqrt(pase_0),
|
||||
mdi_6_newobs_enr=mdi_6_newobs_enr+1)#|>
|
||||
# na.omit()
|
||||
|
||||
bc<-boxcox(mdi_6_newobs_enr~.,data=dta_bc)
|
||||
lambda <- bc$x[which.max(bc$y)]
|
||||
```
|
||||
|
||||
Q-Q plots to compare the two different approaches, and the non-transformed
|
||||
|
||||
```{r}
|
||||
qqnorm(lm(((mdi_6_newobs_enr^lambda-1)/lambda) ~ .,data=dta_bc)$residuals)
|
||||
|
||||
qqnorm(lm(log(mdi_6_newobs_enr) ~ .,data=dta_bc)$residuals)
|
||||
```
|
||||
|
||||
Histograms for reference
|
||||
|
||||
```{r}
|
||||
hist(dta$pase_0,40); hist(sqrt(dta$pase_0),40)
|
||||
```
|
||||
|
||||
```{r}
|
||||
hist(expm1(dta$mdi_6_newobs_enr),40); hist((dta$mdi_6_newobs_enr),40) ## Observed MDI, and log transformed MDI
|
||||
```
|
||||
|
||||
|
||||
|
||||
@ -1,33 +0,0 @@
|
||||
# Regression - sensitivity
|
||||
|
||||
|
||||
outs<-c("mdi_1","mdi_6")
|
||||
|
||||
|
||||
## Non-stratified
|
||||
|
||||
|
||||
source("biv_mul.R")
|
||||
|
||||
bm_16_sens_tbl_rtf <- file("bm_16_sens_tbl.RTF", "w")
|
||||
writeLines(bm_16_tbl%>%as_gt()%>%as_rtf(), bm_16_sens_tbl_rtf)
|
||||
close(bm_16_sens_tbl_rtf)
|
||||
|
||||
bm_16_tbl %>% # build gtsummary table
|
||||
as_gt() %>% # convert to gt table
|
||||
gt::gtsave( # save table as image
|
||||
filename = "bm_16_tbl_sens.png"
|
||||
)
|
||||
|
||||
|
||||
source("biv_mul_strat.R")
|
||||
|
||||
bm_16_tbl_strat_sens_rtf <- file("bm_16_tbl_strat_sens.RTF", "w")
|
||||
writeLines(bm_16_tbl_strat%>%as_gt()%>%as_rtf(), bm_16_tbl_strat_sens_rtf)
|
||||
close(bm_16_tbl_strat_sens_rtf)
|
||||
|
||||
bm_16_tbl_strat %>% # build gtsummary table
|
||||
as_gt() %>% # convert to gt table
|
||||
gt::gtsave( # save table as image
|
||||
filename = "bm_16_tbl_strat_sens.png"
|
||||
)
|
||||
@ -1,34 +0,0 @@
|
||||
|
||||
outs<-c("mdi_1_enr","mdi_6_newobs_enr")
|
||||
|
||||
|
||||
## Non-stratified
|
||||
|
||||
source("biv_mul.R")
|
||||
|
||||
bm_16_tbl_rtf <- file("bm_16_tbl.RTF", "w")
|
||||
writeLines(bm_16_tbl%>%as_gt()%>%as_rtf(), bm_16_tbl_rtf)
|
||||
close(bm_16_tbl_rtf)
|
||||
|
||||
bm_16_tbl %>% # build gtsummary table
|
||||
as_gt() %>% # convert to gt table
|
||||
gt::gtsave( # save table as image
|
||||
filename = "bm_16_tbl.png"
|
||||
)
|
||||
|
||||
|
||||
|
||||
## Stratified by treatment
|
||||
|
||||
|
||||
source("biv_mul_strat.R")
|
||||
|
||||
bm_16_tbl_strat_rtf <- file("bm_16_tbl_strat.RTF", "w")
|
||||
writeLines(bm_16_tbl_strat%>%as_gt()%>%as_rtf(), bm_16_tbl_strat_rtf)
|
||||
close(bm_16_tbl_strat_rtf)
|
||||
|
||||
bm_16_tbl_strat %>% # build gtsummary table
|
||||
as_gt() %>% # convert to gt table
|
||||
gt::gtsave( # save table as image
|
||||
filename = "bm_16_tbl_strat.png"
|
||||
)
|
||||
@ -1,139 +0,0 @@
|
||||
## =============================================================================
|
||||
## Header
|
||||
## =============================================================================
|
||||
|
||||
source("function_trans_cols.R")
|
||||
|
||||
if (trans_vars==TRUE){
|
||||
# If trans_vars flag is TRUE, transform specified variables
|
||||
dta<-trans_cols(dta_backup,sqrts=sqrt_vars,log1ps = log1p_vars_all)
|
||||
} else {dta<-dta_backup}
|
||||
|
||||
library(dplyr)
|
||||
|
||||
source("function_back_trans.R")
|
||||
|
||||
## =============================================================================
|
||||
## Loop
|
||||
## =============================================================================
|
||||
|
||||
bm_list<-list()
|
||||
|
||||
|
||||
for (i in 1:length(outs)){
|
||||
## Bivariate
|
||||
|
||||
dta_l<-dta|>
|
||||
dplyr::select(all_of(c("active_treat",vars,outs[i])))
|
||||
|
||||
sel<-dta_l|>
|
||||
sapply(is.factor)
|
||||
|
||||
# i=1
|
||||
biv<-dta_l|>
|
||||
tbl_uvregression(data=_,
|
||||
y=outs[i],
|
||||
method=lm,
|
||||
label = lab_sel(labels_all,vars),
|
||||
show_single_row=colnames(dta_l)[sel],
|
||||
estimate_fun = ~style_sigfig(.x,digits = 3),
|
||||
pvalue_fun = ~style_pvalue(.x, digits = 3)
|
||||
) |>
|
||||
add_global_p()|>
|
||||
bold_p() #|>
|
||||
# bold_labels() |>
|
||||
# italicize_levels()
|
||||
|
||||
|
||||
# ## What follows is the pragmatic transformation and reformatting
|
||||
#
|
||||
# ## Transforming log1p() to expm1()
|
||||
# biv$table_body$estimate<-expm1(biv$table_body$estimate)
|
||||
# biv$table_body$conf.low<-expm1(biv$table_body$conf.low)
|
||||
# biv$table_body$conf.high<-expm1(biv$table_body$conf.high)
|
||||
#
|
||||
# ## Transforming sqrt() to pase_0^2
|
||||
# biv$table_body$estimate[biv$table_body$variable=="pase_0"]<-
|
||||
# -biv$table_body$estimate[biv$table_body$variable=="pase_0"]^2
|
||||
#
|
||||
# low<-biv$table_body$conf.low[biv$table_body$variable=="pase_0"]^2
|
||||
# high<-biv$table_body$conf.high[biv$table_body$variable=="pase_0"]^2
|
||||
#
|
||||
# biv$table_body$conf.low[biv$table_body$variable=="pase_0"]<-(-low)
|
||||
# biv$table_body$conf.high[biv$table_body$variable=="pase_0"]<-(-high)
|
||||
#
|
||||
# ## Transforming log1p() to expm1()
|
||||
# biv$table_body$estimate[biv$table_body$variable=="nihss_0"]<-
|
||||
# expm1(biv$table_body$estimate[biv$table_body$variable=="nihss_0"])
|
||||
#
|
||||
# low<-expm1(biv$table_body$conf.low[biv$table_body$variable=="nihss_0"])
|
||||
# high<-expm1(biv$table_body$conf.high[biv$table_body$variable=="nihss_0"])
|
||||
#
|
||||
# biv$table_body$conf.low[biv$table_body$variable=="nihss_0"]<-low
|
||||
# biv$table_body$conf.high[biv$table_body$variable=="nihss_0"]<-high
|
||||
#
|
||||
# ## New confidence intervals
|
||||
# # biv$table_body$estimate<-format(biv$table_body$estimate, drop0trailing = F,digits =2)
|
||||
# biv$table_body$ci<-paste(formatC(biv$table_body$conf.low, digits = 3, format = "f"),
|
||||
# formatC(biv$table_body$conf.high, digits = 3, format = "f"),
|
||||
# sep=", ")
|
||||
## multivariate
|
||||
mul<-dta_l |>
|
||||
lm(formula(paste(c(outs[i],"."),collapse="~")),
|
||||
data = _) |>
|
||||
tbl_regression(label = lab_sel(labels_all,vars),
|
||||
show_single_row=colnames(dta_l)[sel],
|
||||
estimate_fun = ~style_sigfig(.x,digits = 3),
|
||||
pvalue_fun = ~style_pvalue(.x, digits = 3)
|
||||
)|>
|
||||
add_n() |>
|
||||
add_global_p() |>
|
||||
bold_p() #|>
|
||||
# bold_labels() |>
|
||||
# italicize_levels()
|
||||
|
||||
if (trans_back==TRUE){
|
||||
ls<-lapply(list(biv,mul), back_trans, outm = "log1p" ,sqrts = "pase_0",log1ps = "nihss_0")
|
||||
} else {ls<-list(biv,mul)}
|
||||
|
||||
## Merge
|
||||
biv_mul<-tbl_merge(
|
||||
tbls = ls,
|
||||
tab_spanner = c("**Bivariate linear regression**",
|
||||
"**Multivariate linear regression**")
|
||||
)
|
||||
|
||||
bm_list[[i]]<-biv_mul
|
||||
}
|
||||
|
||||
## =============================================================================
|
||||
## Big merge
|
||||
## =============================================================================
|
||||
|
||||
if (trans_back==TRUE){tab_span<-c("**One month follow up [TRANS t/r]**",
|
||||
"**Six months follow up [TRANS t/r]**")
|
||||
} else {tab_span<-c("**One month follow up**",
|
||||
"**Six months follow up**")}
|
||||
|
||||
bm_16_tbl<-tbl_merge(
|
||||
tbls = bm_list,
|
||||
tab_spanner = tab_span
|
||||
)
|
||||
bm_16_tbl
|
||||
|
||||
|
||||
fnm<-"bm_16_tbl"
|
||||
if (trans_vars==TRUE){fnm<-paste0(fnm,"_trans")}
|
||||
if (trans_back==TRUE){fnm<-paste0(fnm,"_back")}
|
||||
|
||||
|
||||
bm_16_tbl_rtf <- file(paste0(fnm,".RTF"), "w")
|
||||
writeLines(bm_16_tbl%>%as_gt()%>%as_rtf(), bm_16_tbl_rtf)
|
||||
close(bm_16_tbl_rtf)
|
||||
|
||||
bm_16_tbl %>% # build gtsummary table
|
||||
as_gt() %>% # convert to gt table
|
||||
gt::gtsave( # save table as image
|
||||
filename = paste0(fnm,".png")
|
||||
)
|
||||
|
||||
@ -1,169 +0,0 @@
|
||||
|
||||
|
||||
source("function_trans_cols.R")
|
||||
|
||||
if (trans_vars==TRUE){
|
||||
# If trans_vars flag is TRUE, transform specified variables
|
||||
dta<-trans_cols(dta_backup,sqrts=sqrt_vars,log1ps = log1p_vars_all)
|
||||
} else {dta<-dta_backup}
|
||||
|
||||
library(dplyr)
|
||||
|
||||
source("function_back_trans.R")
|
||||
## =============================================================================
|
||||
## Loop
|
||||
## =============================================================================
|
||||
|
||||
bm_list<-list()
|
||||
|
||||
|
||||
for (i in 1:length(outs)){
|
||||
## Bivariate
|
||||
|
||||
dta_l<-dta|>
|
||||
dplyr::select(all_of(c("active_treat",vars,outs[i])))
|
||||
|
||||
sel<-dta_l|>
|
||||
sapply(is.factor)
|
||||
|
||||
# i=1
|
||||
biv<-dta_l|>
|
||||
tbl_uvregression(data=_,
|
||||
y=outs[i],
|
||||
method=lm,
|
||||
label = lab_sel(labels_all,vars),
|
||||
show_single_row=colnames(dta_l)[sel],
|
||||
estimate_fun = ~style_sigfig(.x,digits = 3),
|
||||
pvalue_fun = ~style_pvalue(.x, digits = 3)
|
||||
) |>
|
||||
add_global_p()|>
|
||||
bold_p() #|>
|
||||
# bold_labels() |>
|
||||
# italicize_levels()
|
||||
|
||||
|
||||
# ## What follows is the pragmatic transformation and reformatting
|
||||
#
|
||||
# ## Transforming log1p() to expm1()
|
||||
# biv$table_body$estimate<-expm1(biv$table_body$estimate)
|
||||
# biv$table_body$conf.low<-expm1(biv$table_body$conf.low)
|
||||
# biv$table_body$conf.high<-expm1(biv$table_body$conf.high)
|
||||
#
|
||||
# ## Transforming sqrt() to pase_0^2
|
||||
# biv$table_body$estimate[biv$table_body$variable=="pase_0"]<-
|
||||
# -biv$table_body$estimate[biv$table_body$variable=="pase_0"]^2
|
||||
#
|
||||
# low<-biv$table_body$conf.low[biv$table_body$variable=="pase_0"]^2
|
||||
# high<-biv$table_body$conf.high[biv$table_body$variable=="pase_0"]^2
|
||||
#
|
||||
# biv$table_body$conf.low[biv$table_body$variable=="pase_0"]<-(-low)
|
||||
# biv$table_body$conf.high[biv$table_body$variable=="pase_0"]<-(-high)
|
||||
#
|
||||
# ## Transforming log1p() to expm1()
|
||||
# biv$table_body$estimate[biv$table_body$variable=="nihss_0"]<-
|
||||
# expm1(biv$table_body$estimate[biv$table_body$variable=="nihss_0"])
|
||||
#
|
||||
# low<-expm1(biv$table_body$conf.low[biv$table_body$variable=="nihss_0"])
|
||||
# high<-expm1(biv$table_body$conf.high[biv$table_body$variable=="nihss_0"])
|
||||
#
|
||||
# biv$table_body$conf.low[biv$table_body$variable=="nihss_0"]<-low
|
||||
# biv$table_body$conf.high[biv$table_body$variable=="nihss_0"]<-high
|
||||
#
|
||||
# ## New confidence intervals
|
||||
# # biv$table_body$estimate<-format(biv$table_body$estimate, drop0trailing = F,digits =2)
|
||||
# biv$table_body$ci<-paste(formatC(biv$table_body$conf.low, digits = 3, format = "f"),
|
||||
# formatC(biv$table_body$conf.high, digits = 3, format = "f"),
|
||||
# sep=", ")
|
||||
## multivariate
|
||||
mul<-dta_l |>
|
||||
lm(formula(paste(c(outs[i],"."),collapse="~")),
|
||||
data = _) |>
|
||||
tbl_regression(label = lab_sel(labels_all,vars),
|
||||
show_single_row=colnames(dta_l)[sel],
|
||||
estimate_fun = ~style_sigfig(.x,digits = 3),
|
||||
pvalue_fun = ~style_pvalue(.x, digits = 3)
|
||||
)|>
|
||||
add_n() |>
|
||||
add_global_p() |>
|
||||
bold_p() #|>
|
||||
# bold_labels() |>
|
||||
# italicize_levels()
|
||||
|
||||
if (trans_back==TRUE){
|
||||
ls<-lapply(list(biv,mul), back_trans, outm = "log1p" ,sqrts = "pase_0",log1ps = "nihss_0")
|
||||
} else {ls<-list(biv,mul)}
|
||||
|
||||
## Merge
|
||||
biv_mul<-tbl_merge(
|
||||
tbls = ls,
|
||||
tab_spanner = c("**Bivariate linear regression**",
|
||||
"**Multivariate linear regression**")
|
||||
)
|
||||
|
||||
bm_list[[i]]<-biv_mul
|
||||
}
|
||||
|
||||
## =============================================================================
|
||||
## Big merge
|
||||
## =============================================================================
|
||||
|
||||
if (trans_back==TRUE){tab_span<-c("**One month follow up [TRANS t/r]**",
|
||||
"**Six months follow up [TRANS t/r]**")
|
||||
} else {tab_span<-c("**One month follow up**",
|
||||
"**Six months follow up**")}
|
||||
|
||||
bm_16_tbl<-tbl_merge(
|
||||
tbls = bm_list,
|
||||
tab_spanner = c("**One month follow up**",
|
||||
"**Six months follow up**")
|
||||
)
|
||||
bm_16_tbl
|
||||
|
||||
|
||||
|
||||
|
||||
bm_16_tbl_rtf <- file("bm_16_tbl_trans_back.RTF", "w")
|
||||
writeLines(bm_16_tbl%>%as_gt()%>%as_rtf(), bm_16_tbl_rtf)
|
||||
close(bm_16_tbl_rtf)
|
||||
|
||||
bm_16_tbl %>% # build gtsummary table
|
||||
as_gt() %>% # convert to gt table
|
||||
gt::gtsave( # save table as image
|
||||
filename = "bm_16_tbl_trans_back.png"
|
||||
)
|
||||
|
||||
|
||||
## Tests and visualisation
|
||||
|
||||
## Box-Cox power transformation performs comparably to logarithmic transformation. The latter is much easier to explain.
|
||||
|
||||
|
||||
library(MASS)
|
||||
dta_bc<-dta_backup|>
|
||||
dplyr::select(all_of(c("mdi_6_newobs_enr",vars)))|>
|
||||
mutate(pase_0=sqrt(pase_0),
|
||||
mdi_6_newobs_enr=mdi_6_newobs_enr+1)#|>
|
||||
# na.omit()
|
||||
|
||||
bc<-boxcox(mdi_6_newobs_enr~.,data=dta_bc)
|
||||
lambda <- bc$x[which.max(bc$y)]
|
||||
|
||||
|
||||
## Q-Q plots to compare the two different approaches, and the non-transformed
|
||||
|
||||
|
||||
q1 <- qqnorm(lm(((mdi_6_newobs_enr^lambda-1)/lambda) ~ .,data=dta_bc)$residuals)
|
||||
|
||||
q2 <- qqnorm(lm(log(mdi_6_newobs_enr) ~ .,data=dta_bc)$residuals)
|
||||
library(patchwork)
|
||||
|
||||
plot(q1); plot(q2)
|
||||
|
||||
## Histograms for reference
|
||||
|
||||
|
||||
h1 <- hist(dta$pase_0,40); hist(sqrt(dta$pase_0),40)
|
||||
|
||||
h2 <- hist(expm1(dta$mdi_6_newobs_enr),40); hist((dta$mdi_6_newobs_enr),40) ## Observed MDI, and log transformed MDI
|
||||
|
||||
plot(h1); plot(h2)
|
||||
146
biv_mul.R
146
biv_mul.R
@ -1,146 +0,0 @@
|
||||
## =============================================================================
|
||||
## Requirements
|
||||
## =============================================================================
|
||||
|
||||
source("function_trans_cols.R")
|
||||
|
||||
if (trans_vars==TRUE){
|
||||
# If trans_vars flag is TRUE, transform specified variables
|
||||
dta<-trans_cols(dta_backup,sqrts=sqrt_vars,log1ps = log1p_vars_all)
|
||||
} else {dta<-dta_backup}
|
||||
|
||||
library(dplyr)
|
||||
library(gtsummary)
|
||||
|
||||
source("function_back_trans.R")
|
||||
|
||||
## =============================================================================
|
||||
## Loop
|
||||
## =============================================================================
|
||||
|
||||
bm_list<-list()
|
||||
|
||||
for (i in 1:length(outs)){
|
||||
|
||||
dta_l<-dta|>
|
||||
dplyr::select(all_of(c("active_treat",vars,outs[i]))) # active_treat should be vector
|
||||
|
||||
sel<-dta_l|>
|
||||
sapply(is.factor)
|
||||
|
||||
## Bivariate
|
||||
if (!is.null(strat_var)){biv<-dta_l |>
|
||||
tbl_strata(
|
||||
strata = all_of(strat_var),
|
||||
.tbl_fun =
|
||||
~ .x %>%
|
||||
tbl_uvregression(data=.,
|
||||
y=outs[i],
|
||||
method=lm,
|
||||
label = lab_sel(labels_all,vars),
|
||||
show_single_row=colnames(dta_l)[sel][-1], # excluding the first, active-treatment, as this is strat
|
||||
estimate_fun = ~style_sigfig(.x,digits = 3),
|
||||
pvalue_fun = ~style_pvalue(.x, digits = 3)
|
||||
)|>
|
||||
add_global_p()|>
|
||||
bold_p() |>
|
||||
bold_labels() |>
|
||||
italicize_levels(),
|
||||
.header = "**{strata}**, N = {n}"
|
||||
)
|
||||
} else {
|
||||
biv<-dta_l|>
|
||||
tbl_uvregression(data=_,
|
||||
y=outs[i],
|
||||
method=lm,
|
||||
label = lab_sel(labels_all,vars),
|
||||
show_single_row=colnames(dta_l)[sel],
|
||||
estimate_fun = ~style_sigfig(.x,digits = 3),
|
||||
pvalue_fun = ~style_pvalue(.x, digits = 3)
|
||||
) |>
|
||||
add_global_p()|>
|
||||
bold_p()
|
||||
}
|
||||
|
||||
## Multivariate
|
||||
if (!is.null(strat_var)){
|
||||
mul<-dta_l |>
|
||||
tbl_strata(
|
||||
strata = all_of(strat_var),
|
||||
.tbl_fun =
|
||||
~ .x %>%
|
||||
lm(formula(paste(c(outs[i],"."),collapse="~")),
|
||||
data = .) |>
|
||||
tbl_regression(label = lab_sel(labels_all,vars),
|
||||
show_single_row=colnames(dta_l)[sel][-1], # excluding the first, active-treatment, as this is strat
|
||||
estimate_fun = ~style_sigfig(.x,digits = 3),
|
||||
pvalue_fun = ~style_pvalue(.x, digits = 3)
|
||||
)|>
|
||||
add_n() |>
|
||||
add_global_p() |>
|
||||
bold_p() |>
|
||||
bold_labels() |>
|
||||
italicize_levels(),
|
||||
.header = "**{strata}**, N = {n}"
|
||||
)
|
||||
} else {
|
||||
mul<-dta_l |>
|
||||
lm(formula(paste(c(outs[i],"."),collapse="~")),
|
||||
data = _) |>
|
||||
tbl_regression(label = lab_sel(labels_all,vars),
|
||||
show_single_row=colnames(dta_l)[sel],
|
||||
estimate_fun = ~style_sigfig(.x,digits = 3),
|
||||
pvalue_fun = ~style_pvalue(.x, digits = 3)
|
||||
)|>
|
||||
add_n() |>
|
||||
add_global_p() |>
|
||||
bold_p()
|
||||
}
|
||||
|
||||
# Back transforming if flag set
|
||||
if (trans_back==TRUE){
|
||||
ls<-lapply(list(biv,mul), back_trans, outm = "log1p" ,sqrts = "pase_0",log1ps = "nihss_0")
|
||||
} else {ls<-list(biv,mul)}
|
||||
|
||||
## Merge
|
||||
biv_mul<-tbl_merge(
|
||||
tbls = ls,
|
||||
tab_spanner = c("**Bivariate linear regression**",
|
||||
"**Multivariate linear regression**")
|
||||
)
|
||||
|
||||
bm_list[[i]]<-biv_mul
|
||||
}
|
||||
|
||||
## =============================================================================
|
||||
## Big merge
|
||||
## =============================================================================
|
||||
|
||||
# Header if back transformed
|
||||
if (trans_back==TRUE){tab_span<-c("**One month follow up [TRANS t/r]**",
|
||||
"**Six months follow up [TRANS t/r]**")
|
||||
} else {tab_span<-c("**One month follow up**",
|
||||
"**Six months follow up**")}
|
||||
|
||||
bm_16_tbl<-tbl_merge(
|
||||
tbls = bm_list,
|
||||
tab_spanner = tab_span
|
||||
)
|
||||
|
||||
## =============================================================================
|
||||
## Print
|
||||
## =============================================================================
|
||||
|
||||
# File name depending onstratification, transformation and back transformation
|
||||
if (print_tbl==TRUE){
|
||||
fnm<-paste0("bm_16_tbl",lbl_x)
|
||||
if (!is.null(strat_var)){fnm<-paste0(fnm,"_strat")}
|
||||
if (trans_vars==TRUE){fnm<-paste0(fnm,"_trans")}
|
||||
if (trans_back==TRUE){fnm<-paste0(fnm,"_back")}
|
||||
|
||||
|
||||
bm_16_tbl_rtf <- file(paste0(fnm,".RTF"), "w")
|
||||
writeLines(bm_16_tbl%>%as_gt()%>%as_rtf(), bm_16_tbl_rtf)
|
||||
close(bm_16_tbl_rtf)
|
||||
|
||||
}
|
||||
115
biv_mul_man.R
115
biv_mul_man.R
@ -1,115 +0,0 @@
|
||||
##
|
||||
## NOTES
|
||||
##
|
||||
## Leaving gtsummary as this is slow and a little inflexible.
|
||||
##
|
||||
|
||||
## =============================================================================
|
||||
## Requirements
|
||||
## =============================================================================
|
||||
|
||||
source("function_trans_cols.R")
|
||||
|
||||
if (trans_vars==TRUE){
|
||||
# If trans_vars flag is TRUE, transform specified variables
|
||||
dta<-trans_cols(dta_backup,sqrts=sqrt_vars,log1ps = log1p_vars_all)
|
||||
} else {dta<-dta_backup}
|
||||
|
||||
library(dplyr)
|
||||
library(gtsummary)
|
||||
|
||||
source("function_back_trans.R")
|
||||
|
||||
vars_all<-c("active_treat",vars)
|
||||
|
||||
## =============================================================================
|
||||
## Loop
|
||||
## =============================================================================
|
||||
|
||||
dec <- 3
|
||||
bm_list<-list()
|
||||
|
||||
if (biv_mul){
|
||||
do_biv=TRUE
|
||||
do_mul=TRUE
|
||||
} else {
|
||||
do_biv=FALSE
|
||||
do_mul=TRUE
|
||||
}
|
||||
|
||||
for (i in 1:length(outs)){
|
||||
|
||||
if (!is.null(strat_var)){
|
||||
ls<-split(dta,dta[strat_var])
|
||||
strat_list<-list()
|
||||
|
||||
for (j in 1:length(ls)){
|
||||
X<-ls[[i]][,vars]
|
||||
y <- ls[[i]][,outs[i]]
|
||||
|
||||
# Flagging factors and continous variables
|
||||
#
|
||||
sel_f<-X|>
|
||||
sapply(is.factor)
|
||||
f.names <- colnames(X)[sel_f]
|
||||
|
||||
source("function_reg_table.R")
|
||||
rt<-reg_table(X,y,m.biv=do_biv,m.mul=do_mul,trans.var = trans_vars,sqrt.vars=sqrt_vars,inter.add=inter_reg)
|
||||
|
||||
rt<-rt|>mutate(across(matches('co|lo|hi|pv'),as.numeric))
|
||||
|
||||
rt[,grepl('co|lo|hi|pv',names(rt))] <- sapply( round( rt[,grepl('co|lo|hi|pv',names(rt))], 3 ),formatC, format='f', digits=3 )
|
||||
|
||||
strat_list[[j]] <- rt
|
||||
names(strat_list)[j] <- levels(dta[,strat_var])[j]
|
||||
|
||||
}
|
||||
|
||||
bm_list[[i]] <- cbind(names(strat_list)[1],strat_list[[1]],names(strat_list)[2],strat_list[[2]])
|
||||
names(bm_list)[i] <- outs[i]
|
||||
|
||||
} else {
|
||||
X<-dta[,vars_all]
|
||||
y <- dta[,outs[i]]
|
||||
|
||||
# Flagging factors and continous variables
|
||||
#
|
||||
sel_f<-X|>
|
||||
sapply(is.factor)
|
||||
f.names <- colnames(X)[sel_f]
|
||||
|
||||
source("function_reg_table.R")
|
||||
rt<-reg_table(X,y,m.biv=do_biv,m.mul=do_mul,trans.var = trans_vars,sqrt.vars=sqrt_vars,inter.add=inter_reg)
|
||||
|
||||
rt<-rt|>mutate(across(matches('co|lo|hi|pv'),as.numeric))
|
||||
|
||||
rt[,grepl('co|lo|hi|pv',names(rt))] <- sapply( round( rt[,grepl('co|lo|hi|pv',names(rt))], 3 ),formatC, format='f', digits=3 )
|
||||
|
||||
bm_list[[i]] <- rt
|
||||
names(bm_list)[i] <- outs[i]
|
||||
|
||||
}
|
||||
|
||||
}
|
||||
## =============================================================================
|
||||
## Print
|
||||
## =============================================================================
|
||||
|
||||
# File name depending onstratification, transformation and back transformation
|
||||
if (print_tbl==TRUE){
|
||||
fnm<-paste0("bm_16_tbl",lbl_x)
|
||||
if (!is.null(strat_var)){fnm<-paste0(fnm,"_strat")}
|
||||
if (trans_vars==TRUE){fnm<-paste0(fnm,"_trans")}
|
||||
# if (trans_back==TRUE){fnm<-paste0(fnm,"_back")}
|
||||
|
||||
export<-data.frame()
|
||||
for (i in 1:length(bm_list)){
|
||||
export<-rbind(export,paste(names(bm_list)[i]),bm_list[[i]])
|
||||
}
|
||||
|
||||
write.csv(export,paste0(fnm,".csv"))
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
@ -1,33 +0,0 @@
|
||||
"","name","pred","co","lo","hi","pv","co.p","lo.p","hi.p"
|
||||
"1","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr"
|
||||
"2","active_treat","active_treat.yes","0.233653892072177","-0.232179668891965","0.699487453036319","0.324864859797705","0.263207210508771","-0.207196331646934","1.01272082910031"
|
||||
"3","pase_0","pase_0","-0.000244585430319758","-0.00187082402407513","0.000143390295747283","0.266354042432291","-0.000244585430319758","-0.00187082402407513","0.000143390295747283"
|
||||
"4","female","female.yes","0.180213913459648","0.0193236851124511","0.341104141806845","0.0282148782420233","0.197473491423575","0.0195115959405863","0.406499708656313"
|
||||
"5","age","age","-0.00615699034671417","-0.0125459725709187","0.000231991877490371","0.0588843723328414","-0.00615699034671417","-0.0125459725709187","0.000231991877490371"
|
||||
"6","cohab","cohab.yes","0.0385997993494245","-0.121911617887899","0.199111216586748","0.636788296013232","0.0393544500801572","-0.114773396757817","0.220317677919905"
|
||||
"7","ever_smoker","ever_smoker.yes","-0.130804728095057","-0.288167087752173","0.0265576315620582","0.103067585262741","-0.122610912897366","-0.250363673274614","0.026913428181286"
|
||||
"8","diabetes","diabetes.yes","0.218436741940986","-0.0112594513032668","0.448132935185239","0.0622889336689862","0.244130312869116","-0.011196300916562","0.565386776803707"
|
||||
"9","hypertension","hypertension.yes","0.0616393238146578","-0.0909735343341647","0.21425218196348","0.427835234030472","0.0635786680438701","-0.0869581251593398","0.238935052475424"
|
||||
"10","afli","afli.yes","-0.151678986134109","-0.353288078026529","0.0499301057583107","0.139996958070073","-0.140735927846055","-0.297625174523839","0.0511976211477106"
|
||||
"11","ami","ami.yes","0.175218683806947","-0.105335118835802","0.455772486449696","0.220374098717126","0.191506751356345","-0.0999771425699263","0.577391426015035"
|
||||
"12","tci","tci.yes","0.0747157782985039","-0.365670936717259","0.515102493314267","0.7390186199739","0.0775778363499326","-0.306268954930611","0.673810047345449"
|
||||
"13","pad","pad.yes","0.150056760624913","-0.237311562420527","0.537425083670353","0.446960209143568","0.161900191037562","-0.211254493727694","0.711593971943397"
|
||||
"14","nihss_0","nihss_0","0.11613724835806","0.00897236714493893","0.22330212957118","0.0337248669412898","0.11613724835806","0.00897236714493893","0.22330212957118"
|
||||
"15","any_reperf","any_reperf.yes","-0.0107151021515006","-0.172236649696012","0.150806445393011","0.89635092823517","-0.0106578999359388","-0.158220057372026","0.162771576504227"
|
||||
"16","active_treat*pase_0+","active_treat*pase_0+","-0.0110727809082654","-0.0493422786913023","0.0271967168747716","0.569968244427194","-0.0110727809082654","-0.0493422786913023","0.0271967168747716"
|
||||
"17","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr"
|
||||
"18","active_treat","active_treat.yes","-0.0564200982921334","-0.623930308211759","0.511090111627492","0.845187999796114","-0.0548580000618283","-0.464165700383232","0.667107538069166"
|
||||
"19","pase_0","pase_0","-0.000976681489563953","-0.00407660143217371","1.80757612451662e-06","0.0601839260699098","-0.000976681489563953","-0.00407660143217371","1.80757612451662e-06"
|
||||
"20","female","female.yes","0.263835454377699","0.0758881335002106","0.451782775255187","0.00603737741261064","0.301913954433282","0.0788418811415876","0.571110627402178"
|
||||
"21","age","age","-0.00707604184801931","-0.0145258759726912","0.000373792276652601","0.0626020368466464","-0.00707604184801931","-0.0145258759726912","0.000373792276652601"
|
||||
"22","cohab","cohab.yes","-0.00811615427982147","-0.197824514236291","0.181592205676648","0.933033500656618","-0.0080833072236699","-0.17948617099948","0.199125097753481"
|
||||
"23","ever_smoker","ever_smoker.yes","-0.000519117102016271","-0.18339011388276","0.182351879678728","0.995551387820496","-0.000518982384045944","-0.167556655320812","0.20003638801342"
|
||||
"24","diabetes","diabetes.yes","-0.0169576554876117","-0.299400688593834","0.26548537761861","0.906128811311326","-0.0168146837437277","-0.258737665440769","0.304063785564845"
|
||||
"25","hypertension","hypertension.yes","0.0997346056783598","-0.0787083358306297","0.278177547187349","0.27262085505285","0.10487765090699","-0.0756905269109551","0.32072066663342"
|
||||
"26","afli","afli.yes","-0.0627807759073845","-0.300670509163122","0.175108957348353","0.604272406167713","-0.0608506647157438","-0.259678338231203","0.191376018712674"
|
||||
"27","ami","ami.yes","0.303152924706539","-0.032166810710295","0.638472660123374","0.0762901279157927","0.354121527246548","-0.0316549617176921","0.893586519331088"
|
||||
"28","tci","tci.yes","-0.695532964813223","-1.21149930597945","-0.179566623646997","0.00835099528356764","-0.501191470265989","-0.70224947454008","-0.164367723781238"
|
||||
"29","pad","pad.yes","0.303632413594264","-0.155473443332633","0.762738270521161","0.194364922678559","0.354770969159139","-0.14399019060515","1.14413942309133"
|
||||
"30","nihss_0","nihss_0","0.133424171870123","0.00625404713700828","0.260594296603237","0.0397906254749111","0.133424171870123","0.00625404713700828","0.260594296603237"
|
||||
"31","any_reperf","any_reperf.yes","-0.0346496766887493","-0.222751967579255","0.153452614201757","0.717519225149966","-0.0340562503960797","-0.199686671668901","0.165852540959894"
|
||||
"32","active_treat*pase_0+","active_treat*pase_0+","-0.0010263702467695","-0.0474599310905048","0.0454071905969658","0.965370876353512","-0.0010263702467695","-0.0474599310905048","0.0454071905969658"
|
||||
|
@ -1,14 +0,0 @@
|
||||
"","name","pred","co","lo","hi","pv","co.p","lo.p","hi.p"
|
||||
"1","nihss_0","nihss_0","nihss_0","nihss_0","nihss_0","nihss_0","nihss_0","nihss_0","nihss_0"
|
||||
"2","active_treat","active_treat.yes","0.002","-0.119","0.123","0.975","0.002","-0.112","0.130"
|
||||
"3","pase_0","pase_0","-0.000","-0.001","0.000","0.067","-0.000","-0.001","0.000"
|
||||
"4","female","female.yes","0.086","-0.050","0.222","0.213","0.090","-0.049","0.249"
|
||||
"5","age","age","0.004","-0.001","0.010","0.127","0.004","-0.001","0.010"
|
||||
"6","cohab","cohab.yes","0.085","-0.049","0.220","0.215","0.089","-0.048","0.246"
|
||||
"7","ever_smoker","ever_smoker.yes","0.008","-0.124","0.140","0.908","0.008","-0.117","0.150"
|
||||
"8","diabetes","diabetes.yes","-0.000","-0.192","0.192","0.999","-0.000","-0.174","0.211"
|
||||
"9","hypertension","hypertension.yes","-0.149","-0.275","-0.022","0.021","-0.138","-0.240","-0.022"
|
||||
"10","afli","afli.yes","0.229","0.063","0.395","0.007","0.258","0.065","0.485"
|
||||
"11","ami","ami.yes","0.034","-0.195","0.263","0.771","0.035","-0.177","0.301"
|
||||
"12","tci","tci.yes","-0.170","-0.535","0.195","0.361","-0.156","-0.414","0.216"
|
||||
"13","pad","pad.yes","-0.273","-0.608","0.062","0.110","-0.239","-0.455","0.063"
|
||||
|
@ -1,31 +0,0 @@
|
||||
"","pred","biv_co","biv_lo","biv_hi","biv_pv","biv_co.p","biv_lo.p","biv_hi.p","mul_co","mul_lo","mul_hi","mul_pv","mul_co.p","mul_lo.p","mul_hi.p"
|
||||
"1","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month"
|
||||
"2","active_treat.yes","0.066","-0.077","0.209","0.367","0.068","-0.074","0.232","0.122","-0.022","0.266","0.096","0.13","-0.022","0.305"
|
||||
"3","afli.yes","-0.159","-0.356","0.037","0.112","-0.147","-0.299","0.038","-0.135","-0.337","0.066","0.188","-0.127","-0.286","0.069"
|
||||
"4","age","-0.002","-0.007","0.004","0.575","-0.002","-0.007","0.004","-0.006","-0.012","0.001","0.073","-0.006","-0.012","0.001"
|
||||
"5","ami.yes","0.143","-0.116","0.402","0.28","0.153","-0.11","0.494","0.166","-0.116","0.447","0.248","0.18","-0.11","0.564"
|
||||
"6","any_reperf.yes","-0.004","-0.151","0.144","0.961","-0.004","-0.141","0.155","-0.024","-0.185","0.138","0.773","-0.023","-0.169","0.147"
|
||||
"7","cohab.yes","0.018","-0.132","0.168","0.814","0.018","-0.124","0.183","0.033","-0.127","0.193","0.687","0.033","-0.12","0.213"
|
||||
"8","diabetes.yes","0.142","-0.083","0.367","0.215","0.153","-0.08","0.444","0.196","-0.036","0.427","0.098","0.216","-0.036","0.533"
|
||||
"9","ever_smoker.yes","-0.093","-0.245","0.059","0.23","-0.089","-0.218","0.061","-0.136","-0.294","0.021","0.09","-0.127","-0.255","0.022"
|
||||
"10","female.yes","0.139","-0.011","0.289","0.07","0.149","-0.011","0.335","0.186","0.025","0.347","0.024","0.204","0.025","0.414"
|
||||
"11","hypertension.yes","0.146","0.003","0.288","0.045","0.157","0.003","0.334","0.071","-0.082","0.223","0.361","0.073","-0.078","0.25"
|
||||
"12","nihss_0","0.069","-0.031","0.169","0.178","0.069","-0.031","0.169","0.09","-0.018","0.198","0.101","0.09","-0.018","0.198"
|
||||
"13","pad.yes","0.16","-0.195","0.515","0.376","0.173","-0.177","0.674","0.166","-0.219","0.551","0.398","0.18","-0.197","0.735"
|
||||
"14","pase_0","-0.001","-0.002","0","0.013","-0.001","-0.002","0","0","-0.002","0","0.105","0","-0.002","0"
|
||||
"15","tci.yes","0.159","-0.291","0.608","0.489","0.172","-0.253","0.837","0.102","-0.336","0.54","0.647","0.107","-0.285","0.715"
|
||||
"16","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month"
|
||||
"17","active_treat.yes","-0.081","-0.246","0.083","0.331","-0.078","-0.218","0.087","-0.054","-0.223","0.114","0.528","-0.053","-0.2","0.121"
|
||||
"18","afli.yes","-0.042","-0.273","0.189","0.719","-0.041","-0.239","0.208","-0.077","-0.317","0.162","0.526","-0.074","-0.271","0.176"
|
||||
"19","age","0.001","-0.005","0.008","0.744","0.001","-0.005","0.008","-0.007","-0.014","0.001","0.069","-0.007","-0.014","0.001"
|
||||
"20","ami.yes","0.156","-0.15","0.463","0.317","0.169","-0.14","0.589","0.312","-0.021","0.645","0.066","0.366","-0.021","0.905"
|
||||
"21","any_reperf.yes","-0.011","-0.181","0.158","0.896","-0.011","-0.165","0.171","-0.021","-0.209","0.166","0.823","-0.021","-0.188","0.181"
|
||||
"22","cohab.yes","-0.108","-0.282","0.066","0.221","-0.103","-0.246","0.068","-0.047","-0.236","0.143","0.629","-0.045","-0.21","0.154"
|
||||
"23","diabetes.yes","-0.021","-0.296","0.253","0.879","-0.021","-0.256","0.288","-0.068","-0.353","0.217","0.641","-0.066","-0.298","0.243"
|
||||
"24","ever_smoker.yes","-0.033","-0.209","0.143","0.714","-0.032","-0.188","0.154","-0.008","-0.191","0.174","0.927","-0.008","-0.174","0.19"
|
||||
"25","female.yes","0.252","0.081","0.423","0.004","0.287","0.084","0.527","0.25","0.062","0.438","0.009","0.284","0.064","0.549"
|
||||
"26","hypertension.yes","0.231","0.067","0.396","0.006","0.26","0.07","0.485","0.122","-0.056","0.301","0.177","0.13","-0.054","0.351"
|
||||
"27","nihss_0","0.13","0.012","0.247","0.031","0.13","0.012","0.247","0.122","-0.005","0.249","0.06","0.122","-0.005","0.249"
|
||||
"28","pad.yes","0.39","-0.03","0.811","0.069","0.478","-0.03","1.25","0.325","-0.132","0.781","0.163","0.384","-0.123","1.184"
|
||||
"29","pase_0","-0.001","-0.003","0","0.014","-0.001","-0.003","0","-0.001","-0.003","0","0.047","-0.001","-0.003","0"
|
||||
"30","tci.yes","-0.637","-1.153","-0.122","0.016","-0.471","-0.684","-0.115","-0.674","-1.186","-0.161","0.01","-0.49","-0.694","-0.149"
|
||||
|
@ -1,29 +0,0 @@
|
||||
"","names(strat_list)[1]","name","pred","biv_co","biv_lo","biv_hi","biv_pv","biv_co.p","biv_lo.p","biv_hi.p","mul_co","mul_lo","mul_hi","mul_pv","mul_co.p","mul_lo.p","mul_hi.p","names(strat_list)[2]","name","pred","biv_co","biv_lo","biv_hi","biv_pv","biv_co.p","biv_lo.p","biv_hi.p","mul_co","mul_lo","mul_hi","mul_pv","mul_co.p","mul_lo.p","mul_hi.p"
|
||||
"1","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr"
|
||||
"2","no","pase_0","pase_0","-0.000","-0.002","0.000","0.226","-0.000","-0.002","0.000","-0.000","-0.002","0.000","0.364","-0.000","-0.002","0.000","yes","pase_0","pase_0","-0.000","-0.002","0.000","0.226","-0.000","-0.002","0.000","-0.000","-0.002","0.000","0.364","-0.000","-0.002","0.000"
|
||||
"3","no","female","female.yes","-0.032","-0.245","0.180","0.765","-0.032","-0.217","0.198","0.025","-0.204","0.255","0.830","0.025","-0.185","0.290","yes","female","female.yes","-0.032","-0.245","0.180","0.765","-0.032","-0.217","0.198","0.025","-0.204","0.255","0.830","0.025","-0.185","0.290"
|
||||
"4","no","age","age","0.001","-0.007","0.009","0.847","0.001","-0.007","0.009","-0.001","-0.010","0.008","0.768","-0.001","-0.010","0.008","yes","age","age","0.001","-0.007","0.009","0.847","0.001","-0.007","0.009","-0.001","-0.010","0.008","0.768","-0.001","-0.010","0.008"
|
||||
"5","no","cohab","cohab.yes","-0.028","-0.235","0.180","0.792","-0.027","-0.210","0.197","0.039","-0.188","0.266","0.736","0.040","-0.171","0.304","yes","cohab","cohab.yes","-0.028","-0.235","0.180","0.792","-0.027","-0.210","0.197","0.039","-0.188","0.266","0.736","0.040","-0.171","0.304"
|
||||
"6","no","ever_smoker","ever_smoker.yes","-0.115","-0.333","0.103","0.301","-0.109","-0.283","0.109","-0.114","-0.341","0.113","0.323","-0.108","-0.289","0.119","yes","ever_smoker","ever_smoker.yes","-0.115","-0.333","0.103","0.301","-0.109","-0.283","0.109","-0.114","-0.341","0.113","0.323","-0.108","-0.289","0.119"
|
||||
"7","no","diabetes","diabetes.yes","0.125","-0.162","0.412","0.392","0.133","-0.149","0.509","0.176","-0.130","0.482","0.259","0.192","-0.122","0.619","yes","diabetes","diabetes.yes","0.125","-0.162","0.412","0.392","0.133","-0.149","0.509","0.176","-0.130","0.482","0.259","0.192","-0.122","0.619"
|
||||
"8","no","hypertension","hypertension.yes","0.064","-0.134","0.262","0.525","0.066","-0.126","0.300","-0.054","-0.272","0.164","0.625","-0.053","-0.238","0.178","yes","hypertension","hypertension.yes","0.064","-0.134","0.262","0.525","0.066","-0.126","0.300","-0.054","-0.272","0.164","0.625","-0.053","-0.238","0.178"
|
||||
"9","no","afli","afli.yes","-0.061","-0.337","0.216","0.665","-0.059","-0.286","0.241","-0.126","-0.418","0.167","0.397","-0.118","-0.342","0.181","yes","afli","afli.yes","-0.061","-0.337","0.216","0.665","-0.059","-0.286","0.241","-0.126","-0.418","0.167","0.397","-0.118","-0.342","0.181"
|
||||
"10","no","ami","ami.yes","0.230","-0.116","0.575","0.192","0.258","-0.110","0.778","0.348","-0.044","0.741","0.082","0.416","-0.043","1.097","yes","ami","ami.yes","0.230","-0.116","0.575","0.192","0.258","-0.110","0.778","0.348","-0.044","0.741","0.082","0.416","-0.043","1.097"
|
||||
"11","no","tci","tci.yes","-0.371","-1.065","0.324","0.295","-0.310","-0.655","0.383","-0.356","-1.044","0.332","0.309","-0.300","-0.648","0.393","yes","tci","tci.yes","-0.371","-1.065","0.324","0.295","-0.310","-0.655","0.383","-0.356","-1.044","0.332","0.309","-0.300","-0.648","0.393"
|
||||
"12","no","pad","pad.yes","0.321","-0.111","0.752","0.145","0.378","-0.105","1.122","0.287","-0.195","0.770","0.241","0.333","-0.177","1.159","yes","pad","pad.yes","0.321","-0.111","0.752","0.145","0.378","-0.105","1.122","0.287","-0.195","0.770","0.241","0.333","-0.177","1.159"
|
||||
"13","no","nihss_0","nihss_0","0.153","0.010","0.297","0.036","0.153","0.010","0.297","0.238","0.083","0.393","0.003","0.238","0.083","0.393","yes","nihss_0","nihss_0","0.153","0.010","0.297","0.036","0.153","0.010","0.297","0.238","0.083","0.393","0.003","0.238","0.083","0.393"
|
||||
"14","no","any_reperf","any_reperf.yes","-0.087","-0.289","0.114","0.396","-0.083","-0.251","0.121","-0.194","-0.417","0.029","0.089","-0.176","-0.341","0.030","yes","any_reperf","any_reperf.yes","-0.087","-0.289","0.114","0.396","-0.083","-0.251","0.121","-0.194","-0.417","0.029","0.089","-0.176","-0.341","0.030"
|
||||
"15","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr"
|
||||
"16","no","pase_0","pase_0","-0.002","-0.006","-0.000","0.007","-0.002","-0.006","-0.000","-0.002","-0.006","0.000","0.059","-0.002","-0.006","0.000","yes","pase_0","pase_0","-0.002","-0.006","-0.000","0.007","-0.002","-0.006","-0.000","-0.002","-0.006","0.000","0.059","-0.002","-0.006","0.000"
|
||||
"17","no","female","female.yes","0.305","0.061","0.549","0.015","0.356","0.063","0.731","0.274","0.002","0.546","0.048","0.315","0.002","0.726","yes","female","female.yes","0.305","0.061","0.549","0.015","0.356","0.063","0.731","0.274","0.002","0.546","0.048","0.315","0.002","0.726"
|
||||
"18","no","age","age","0.000","-0.009","0.009","0.977","0.000","-0.009","0.009","-0.008","-0.019","0.003","0.134","-0.008","-0.019","0.003","yes","age","age","0.000","-0.009","0.009","0.977","0.000","-0.009","0.009","-0.008","-0.019","0.003","0.134","-0.008","-0.019","0.003"
|
||||
"19","no","cohab","cohab.yes","-0.067","-0.325","0.190","0.607","-0.065","-0.277","0.209","-0.102","-0.381","0.178","0.475","-0.097","-0.317","0.195","yes","cohab","cohab.yes","-0.067","-0.325","0.190","0.607","-0.065","-0.277","0.209","-0.102","-0.381","0.178","0.475","-0.097","-0.317","0.195"
|
||||
"20","no","ever_smoker","ever_smoker.yes","0.103","-0.145","0.351","0.413","0.109","-0.135","0.420","0.098","-0.163","0.359","0.460","0.103","-0.150","0.432","yes","ever_smoker","ever_smoker.yes","0.103","-0.145","0.351","0.413","0.109","-0.135","0.420","0.098","-0.163","0.359","0.460","0.103","-0.150","0.432"
|
||||
"21","no","diabetes","diabetes.yes","0.080","-0.339","0.498","0.709","0.083","-0.287","0.645","0.079","-0.343","0.500","0.713","0.082","-0.290","0.649","yes","diabetes","diabetes.yes","0.080","-0.339","0.498","0.709","0.083","-0.287","0.645","0.079","-0.343","0.500","0.713","0.082","-0.290","0.649"
|
||||
"22","no","hypertension","hypertension.yes","0.285","0.047","0.523","0.019","0.330","0.049","0.686","0.158","-0.103","0.419","0.235","0.171","-0.098","0.520","yes","hypertension","hypertension.yes","0.285","0.047","0.523","0.019","0.330","0.049","0.686","0.158","-0.103","0.419","0.235","0.171","-0.098","0.520"
|
||||
"23","no","afli","afli.yes","-0.079","-0.404","0.245","0.631","-0.076","-0.332","0.278","-0.076","-0.411","0.259","0.655","-0.073","-0.337","0.296","yes","afli","afli.yes","-0.079","-0.404","0.245","0.631","-0.076","-0.332","0.278","-0.076","-0.411","0.259","0.655","-0.073","-0.337","0.296"
|
||||
"24","no","ami","ami.yes","0.203","-0.254","0.661","0.382","0.225","-0.225","0.936","0.178","-0.326","0.682","0.488","0.195","-0.278","0.977","yes","ami","ami.yes","0.203","-0.254","0.661","0.382","0.225","-0.225","0.936","0.178","-0.326","0.682","0.488","0.195","-0.278","0.977"
|
||||
"25","no","tci","tci.yes","-0.718","-1.431","-0.005","0.049","-0.512","-0.761","-0.005","-0.806","-1.521","-0.092","0.027","-0.553","-0.781","-0.088","yes","tci","tci.yes","-0.718","-1.431","-0.005","0.049","-0.512","-0.761","-0.005","-0.806","-1.521","-0.092","0.027","-0.553","-0.781","-0.088"
|
||||
"26","no","pad","pad.yes","0.339","-0.439","1.118","0.391","0.404","-0.355","2.058","0.293","-0.575","1.161","0.507","0.340","-0.437","2.194","yes","pad","pad.yes","0.339","-0.439","1.118","0.391","0.404","-0.355","2.058","0.293","-0.575","1.161","0.507","0.340","-0.437","2.194"
|
||||
"27","no","nihss_0","nihss_0","0.028","-0.136","0.193","0.734","0.028","-0.136","0.193","-0.056","-0.239","0.126","0.544","-0.056","-0.239","0.126","yes","nihss_0","nihss_0","0.028","-0.136","0.193","0.734","0.028","-0.136","0.193","-0.056","-0.239","0.126","0.544","-0.056","-0.239","0.126"
|
||||
"28","no","any_reperf","any_reperf.yes","0.037","-0.211","0.285","0.768","0.038","-0.190","0.330","0.183","-0.097","0.462","0.199","0.201","-0.092","0.588","yes","any_reperf","any_reperf.yes","0.037","-0.211","0.285","0.768","0.038","-0.190","0.330","0.183","-0.097","0.462","0.199","0.201","-0.092","0.588"
|
||||
|
@ -1,29 +0,0 @@
|
||||
"","name","pred","biv_co","biv_lo","biv_hi","biv_pv","biv_co.p","biv_lo.p","biv_hi.p","mul_co","mul_lo","mul_hi","mul_pv","mul_co.p","mul_lo.p","mul_hi.p","names(strat_list)[2]","name.1","pred.1","biv_co.1","biv_lo.1","biv_hi.1","biv_pv.1","biv_co.p.1","biv_lo.p.1","biv_hi.p.1","mul_co.1","mul_lo.1","mul_hi.1","mul_pv.1","mul_co.p.1","mul_lo.p.1","mul_hi.p.1"
|
||||
"1","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month"
|
||||
"2","afli","afli.yes","-0.061","-0.337","0.216","0.665","-0.059","-0.286","0.241","-0.126","-0.418","0.167","0.397","-0.118","-0.342","0.181","yes","afli","afli.yes","-0.061","-0.337","0.216","0.665","-0.059","-0.286","0.241","-0.126","-0.418","0.167","0.397","-0.118","-0.342","0.181"
|
||||
"3","age","age","0.001","-0.007","0.009","0.847","0.001","-0.007","0.009","-0.001","-0.01","0.008","0.768","-0.001","-0.01","0.008","yes","age","age","0.001","-0.007","0.009","0.847","0.001","-0.007","0.009","-0.001","-0.01","0.008","0.768","-0.001","-0.01","0.008"
|
||||
"4","ami","ami.yes","0.23","-0.116","0.575","0.192","0.258","-0.11","0.778","0.348","-0.044","0.741","0.082","0.416","-0.043","1.097","yes","ami","ami.yes","0.23","-0.116","0.575","0.192","0.258","-0.11","0.778","0.348","-0.044","0.741","0.082","0.416","-0.043","1.097"
|
||||
"5","any_reperf","any_reperf.yes","-0.087","-0.289","0.114","0.396","-0.083","-0.251","0.121","-0.194","-0.417","0.029","0.089","-0.176","-0.341","0.03","yes","any_reperf","any_reperf.yes","-0.087","-0.289","0.114","0.396","-0.083","-0.251","0.121","-0.194","-0.417","0.029","0.089","-0.176","-0.341","0.03"
|
||||
"6","cohab","cohab.yes","-0.028","-0.235","0.18","0.792","-0.027","-0.21","0.197","0.039","-0.188","0.266","0.736","0.04","-0.171","0.304","yes","cohab","cohab.yes","-0.028","-0.235","0.18","0.792","-0.027","-0.21","0.197","0.039","-0.188","0.266","0.736","0.04","-0.171","0.304"
|
||||
"7","diabetes","diabetes.yes","0.125","-0.162","0.412","0.392","0.133","-0.149","0.509","0.176","-0.13","0.482","0.259","0.192","-0.122","0.619","yes","diabetes","diabetes.yes","0.125","-0.162","0.412","0.392","0.133","-0.149","0.509","0.176","-0.13","0.482","0.259","0.192","-0.122","0.619"
|
||||
"8","ever_smoker","ever_smoker.yes","-0.115","-0.333","0.103","0.301","-0.109","-0.283","0.109","-0.114","-0.341","0.113","0.323","-0.108","-0.289","0.119","yes","ever_smoker","ever_smoker.yes","-0.115","-0.333","0.103","0.301","-0.109","-0.283","0.109","-0.114","-0.341","0.113","0.323","-0.108","-0.289","0.119"
|
||||
"9","female","female.yes","-0.032","-0.245","0.18","0.765","-0.032","-0.217","0.198","0.025","-0.204","0.255","0.83","0.025","-0.185","0.29","yes","female","female.yes","-0.032","-0.245","0.18","0.765","-0.032","-0.217","0.198","0.025","-0.204","0.255","0.83","0.025","-0.185","0.29"
|
||||
"10","hypertension","hypertension.yes","0.064","-0.134","0.262","0.525","0.066","-0.126","0.3","-0.054","-0.272","0.164","0.625","-0.053","-0.238","0.178","yes","hypertension","hypertension.yes","0.064","-0.134","0.262","0.525","0.066","-0.126","0.3","-0.054","-0.272","0.164","0.625","-0.053","-0.238","0.178"
|
||||
"11","nihss_0","nihss_0","0.153","0.01","0.297","0.036","0.153","0.01","0.297","0.238","0.083","0.393","0.003","0.238","0.083","0.393","yes","nihss_0","nihss_0","0.153","0.01","0.297","0.036","0.153","0.01","0.297","0.238","0.083","0.393","0.003","0.238","0.083","0.393"
|
||||
"12","pad","pad.yes","0.321","-0.111","0.752","0.145","0.378","-0.105","1.122","0.287","-0.195","0.77","0.241","0.333","-0.177","1.159","yes","pad","pad.yes","0.321","-0.111","0.752","0.145","0.378","-0.105","1.122","0.287","-0.195","0.77","0.241","0.333","-0.177","1.159"
|
||||
"13","pase_0","pase_0","0","-0.002","0","0.226","0","-0.002","0","0","-0.002","0","0.364","0","-0.002","0","yes","pase_0","pase_0","0","-0.002","0","0.226","0","-0.002","0","0","-0.002","0","0.364","0","-0.002","0"
|
||||
"14","tci","tci.yes","-0.371","-1.065","0.324","0.295","-0.31","-0.655","0.383","-0.356","-1.044","0.332","0.309","-0.3","-0.648","0.393","yes","tci","tci.yes","-0.371","-1.065","0.324","0.295","-0.31","-0.655","0.383","-0.356","-1.044","0.332","0.309","-0.3","-0.648","0.393"
|
||||
"15","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month"
|
||||
"16","afli","afli.yes","-0.079","-0.404","0.245","0.631","-0.076","-0.332","0.278","-0.076","-0.411","0.259","0.655","-0.073","-0.337","0.296","yes","afli","afli.yes","-0.079","-0.404","0.245","0.631","-0.076","-0.332","0.278","-0.076","-0.411","0.259","0.655","-0.073","-0.337","0.296"
|
||||
"17","age","age","0","-0.009","0.009","0.977","0","-0.009","0.009","-0.008","-0.019","0.003","0.134","-0.008","-0.019","0.003","yes","age","age","0","-0.009","0.009","0.977","0","-0.009","0.009","-0.008","-0.019","0.003","0.134","-0.008","-0.019","0.003"
|
||||
"18","ami","ami.yes","0.203","-0.254","0.661","0.382","0.225","-0.225","0.936","0.178","-0.326","0.682","0.488","0.195","-0.278","0.977","yes","ami","ami.yes","0.203","-0.254","0.661","0.382","0.225","-0.225","0.936","0.178","-0.326","0.682","0.488","0.195","-0.278","0.977"
|
||||
"19","any_reperf","any_reperf.yes","0.037","-0.211","0.285","0.768","0.038","-0.19","0.33","0.183","-0.097","0.462","0.199","0.201","-0.092","0.588","yes","any_reperf","any_reperf.yes","0.037","-0.211","0.285","0.768","0.038","-0.19","0.33","0.183","-0.097","0.462","0.199","0.201","-0.092","0.588"
|
||||
"20","cohab","cohab.yes","-0.067","-0.325","0.19","0.607","-0.065","-0.277","0.209","-0.102","-0.381","0.178","0.475","-0.097","-0.317","0.195","yes","cohab","cohab.yes","-0.067","-0.325","0.19","0.607","-0.065","-0.277","0.209","-0.102","-0.381","0.178","0.475","-0.097","-0.317","0.195"
|
||||
"21","diabetes","diabetes.yes","0.08","-0.339","0.498","0.709","0.083","-0.287","0.645","0.079","-0.343","0.5","0.713","0.082","-0.29","0.649","yes","diabetes","diabetes.yes","0.08","-0.339","0.498","0.709","0.083","-0.287","0.645","0.079","-0.343","0.5","0.713","0.082","-0.29","0.649"
|
||||
"22","ever_smoker","ever_smoker.yes","0.103","-0.145","0.351","0.413","0.109","-0.135","0.42","0.098","-0.163","0.359","0.46","0.103","-0.15","0.432","yes","ever_smoker","ever_smoker.yes","0.103","-0.145","0.351","0.413","0.109","-0.135","0.42","0.098","-0.163","0.359","0.46","0.103","-0.15","0.432"
|
||||
"23","female","female.yes","0.305","0.061","0.549","0.015","0.356","0.063","0.731","0.274","0.002","0.546","0.048","0.315","0.002","0.726","yes","female","female.yes","0.305","0.061","0.549","0.015","0.356","0.063","0.731","0.274","0.002","0.546","0.048","0.315","0.002","0.726"
|
||||
"24","hypertension","hypertension.yes","0.285","0.047","0.523","0.019","0.33","0.049","0.686","0.158","-0.103","0.419","0.235","0.171","-0.098","0.52","yes","hypertension","hypertension.yes","0.285","0.047","0.523","0.019","0.33","0.049","0.686","0.158","-0.103","0.419","0.235","0.171","-0.098","0.52"
|
||||
"25","nihss_0","nihss_0","0.028","-0.136","0.193","0.734","0.028","-0.136","0.193","-0.056","-0.239","0.126","0.544","-0.056","-0.239","0.126","yes","nihss_0","nihss_0","0.028","-0.136","0.193","0.734","0.028","-0.136","0.193","-0.056","-0.239","0.126","0.544","-0.056","-0.239","0.126"
|
||||
"26","pad","pad.yes","0.339","-0.439","1.118","0.391","0.404","-0.355","2.058","0.293","-0.575","1.161","0.507","0.34","-0.437","2.194","yes","pad","pad.yes","0.339","-0.439","1.118","0.391","0.404","-0.355","2.058","0.293","-0.575","1.161","0.507","0.34","-0.437","2.194"
|
||||
"27","pase_0","pase_0","-0.002","-0.006","0","0.007","-0.002","-0.006","0","-0.002","-0.006","0","0.059","-0.002","-0.006","0","yes","pase_0","pase_0","-0.002","-0.006","0","0.007","-0.002","-0.006","0","-0.002","-0.006","0","0.059","-0.002","-0.006","0"
|
||||
"28","tci","tci.yes","-0.718","-1.431","-0.005","0.049","-0.512","-0.761","-0.005","-0.806","-1.521","-0.092","0.027","-0.553","-0.781","-0.088","yes","tci","tci.yes","-0.718","-1.431","-0.005","0.049","-0.512","-0.761","-0.005","-0.806","-1.521","-0.092","0.027","-0.553","-0.781","-0.088"
|
||||
|
@ -1,31 +0,0 @@
|
||||
"","name","pred","biv_co","biv_lo","biv_hi","biv_pv","biv_co.p","biv_lo.p","biv_hi.p","mul_co","mul_lo","mul_hi","mul_pv","mul_co.p","mul_lo.p","mul_hi.p"
|
||||
"1","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr","mdi_1_enr"
|
||||
"2","active_treat","active_treat.yes","0.053","-0.090","0.196","0.464","0.055","-0.086","0.217","0.105","-0.039","0.250","0.151","0.111","-0.038","0.284"
|
||||
"3","pase_0","pase_0","-0.001","-0.002","-0.000","0.006","-0.001","-0.002","-0.000","-0.000","-0.002","0.000","0.059","-0.000","-0.002","0.000"
|
||||
"4","female","female.yes","0.145","-0.005","0.295","0.058","0.156","-0.005","0.343","0.183","0.023","0.344","0.025","0.201","0.023","0.410"
|
||||
"5","age","age","-0.001","-0.007","0.004","0.688","-0.001","-0.007","0.004","-0.006","-0.012","0.000","0.060","-0.006","-0.012","0.000"
|
||||
"6","cohab","cohab.yes","0.019","-0.132","0.169","0.807","0.019","-0.123","0.184","0.040","-0.120","0.201","0.620","0.041","-0.113","0.222"
|
||||
"7","ever_smoker","ever_smoker.yes","-0.088","-0.240","0.064","0.256","-0.084","-0.214","0.066","-0.134","-0.291","0.023","0.095","-0.125","-0.252","0.023"
|
||||
"8","diabetes","diabetes.yes","0.159","-0.065","0.383","0.163","0.172","-0.063","0.466","0.217","-0.013","0.446","0.064","0.242","-0.012","0.563"
|
||||
"9","hypertension","hypertension.yes","0.138","-0.004","0.281","0.058","0.148","-0.004","0.324","0.061","-0.091","0.214","0.430","0.063","-0.087","0.238"
|
||||
"10","afli","afli.yes","-0.146","-0.342","0.050","0.143","-0.136","-0.290","0.051","-0.149","-0.351","0.052","0.146","-0.139","-0.296","0.053"
|
||||
"11","ami","ami.yes","0.147","-0.112","0.405","0.266","0.158","-0.106","0.499","0.177","-0.103","0.457","0.216","0.194","-0.098","0.580"
|
||||
"12","tci","tci.yes","0.138","-0.316","0.591","0.550","0.148","-0.271","0.807","0.078","-0.362","0.518","0.729","0.081","-0.304","0.678"
|
||||
"13","pad","pad.yes","0.139","-0.219","0.497","0.447","0.149","-0.197","0.643","0.150","-0.237","0.537","0.446","0.162","-0.211","0.711"
|
||||
"14","nihss_0","nihss_0","0.106","0.007","0.204","0.036","0.106","0.007","0.204","0.117","0.010","0.224","0.032","0.117","0.010","0.224"
|
||||
"15","any_reperf","any_reperf.yes","0.021","-0.127","0.168","0.785","0.021","-0.119","0.183","-0.013","-0.174","0.149","0.879","-0.012","-0.160","0.160"
|
||||
"16","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr","mdi_6_newobs_enr"
|
||||
"17","active_treat","active_treat.yes","-0.084","-0.249","0.081","0.316","-0.081","-0.220","0.084","-0.068","-0.237","0.100","0.426","-0.066","-0.211","0.105"
|
||||
"18","pase_0","pase_0","-0.001","-0.003","-0.000","0.002","-0.001","-0.003","-0.000","-0.001","-0.003","-0.000","0.017","-0.001","-0.003","-0.000"
|
||||
"19","female","female.yes","0.246","0.074","0.418","0.005","0.279","0.077","0.519","0.264","0.077","0.451","0.006","0.302","0.080","0.570"
|
||||
"20","age","age","0.001","-0.005","0.008","0.658","0.001","-0.005","0.008","-0.007","-0.015","0.000","0.062","-0.007","-0.015","0.000"
|
||||
"21","cohab","cohab.yes","-0.094","-0.269","0.080","0.290","-0.090","-0.236","0.084","-0.008","-0.197","0.181","0.935","-0.008","-0.179","0.199"
|
||||
"22","ever_smoker","ever_smoker.yes","-0.021","-0.197","0.155","0.817","-0.020","-0.178","0.168","-0.001","-0.183","0.182","0.994","-0.001","-0.167","0.199"
|
||||
"23","diabetes","diabetes.yes","0.012","-0.260","0.284","0.932","0.012","-0.229","0.329","-0.017","-0.299","0.265","0.905","-0.017","-0.259","0.303"
|
||||
"24","hypertension","hypertension.yes","0.232","0.068","0.396","0.006","0.261","0.070","0.487","0.100","-0.078","0.278","0.272","0.105","-0.075","0.321"
|
||||
"25","afli","afli.yes","-0.025","-0.255","0.206","0.835","-0.024","-0.225","0.229","-0.063","-0.300","0.175","0.605","-0.061","-0.259","0.191"
|
||||
"26","ami","ami.yes","0.200","-0.103","0.502","0.195","0.221","-0.098","0.652","0.303","-0.031","0.638","0.076","0.354","-0.031","0.893"
|
||||
"27","tci","tci.yes","-0.660","-1.180","-0.140","0.013","-0.483","-0.693","-0.130","-0.695","-1.210","-0.180","0.008","-0.501","-0.702","-0.165"
|
||||
"28","pad","pad.yes","0.416","0.001","0.831","0.050","0.516","0.001","1.296","0.304","-0.155","0.762","0.194","0.355","-0.143","1.143"
|
||||
"29","nihss_0","nihss_0","0.142","0.024","0.260","0.018","0.142","0.024","0.260","0.134","0.007","0.260","0.039","0.134","0.007","0.260"
|
||||
"30","any_reperf","any_reperf.yes","-0.028","-0.198","0.142","0.749","-0.027","-0.179","0.153","-0.035","-0.222","0.153","0.715","-0.034","-0.199","0.165"
|
||||
|
@ -1,31 +0,0 @@
|
||||
"","pred","biv_co","biv_lo","biv_hi","biv_pv","biv_co.p","biv_lo.p","biv_hi.p","mul_co","mul_lo","mul_hi","mul_pv","mul_co.p","mul_lo.p","mul_hi.p"
|
||||
"1","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month","One month"
|
||||
"2","active_treat.yes","0.053","-0.09","0.196","0.464","0.055","-0.086","0.217","0.105","-0.039","0.25","0.151","0.111","-0.038","0.284"
|
||||
"3","afli.yes","-0.146","-0.342","0.05","0.143","-0.136","-0.29","0.051","-0.149","-0.351","0.052","0.146","-0.139","-0.296","0.053"
|
||||
"4","age","-0.001","-0.007","0.004","0.688","-0.001","-0.007","0.004","-0.006","-0.012","0","0.06","-0.006","-0.012","0"
|
||||
"5","ami.yes","0.147","-0.112","0.405","0.266","0.158","-0.106","0.499","0.177","-0.103","0.457","0.216","0.194","-0.098","0.58"
|
||||
"6","any_reperf.yes","0.021","-0.127","0.168","0.785","0.021","-0.119","0.183","-0.013","-0.174","0.149","0.879","-0.012","-0.16","0.16"
|
||||
"7","cohab.yes","0.019","-0.132","0.169","0.807","0.019","-0.123","0.184","0.04","-0.12","0.201","0.62","0.041","-0.113","0.222"
|
||||
"8","diabetes.yes","0.159","-0.065","0.383","0.163","0.172","-0.063","0.466","0.217","-0.013","0.446","0.064","0.242","-0.012","0.563"
|
||||
"9","ever_smoker.yes","-0.088","-0.24","0.064","0.256","-0.084","-0.214","0.066","-0.134","-0.291","0.023","0.095","-0.125","-0.252","0.023"
|
||||
"10","female.yes","0.145","-0.005","0.295","0.058","0.156","-0.005","0.343","0.183","0.023","0.344","0.025","0.201","0.023","0.41"
|
||||
"11","hypertension.yes","0.138","-0.004","0.281","0.058","0.148","-0.004","0.324","0.061","-0.091","0.214","0.43","0.063","-0.087","0.238"
|
||||
"12","nihss_0","0.106","0.007","0.204","0.036","0.106","0.007","0.204","0.117","0.01","0.224","0.032","0.117","0.01","0.224"
|
||||
"13","pad.yes","0.139","-0.219","0.497","0.447","0.149","-0.197","0.643","0.15","-0.237","0.537","0.446","0.162","-0.211","0.711"
|
||||
"14","pase_0","-0.001","-0.002","0","0.006","-0.001","-0.002","0","0","-0.002","0","0.059","0","-0.002","0"
|
||||
"15","tci.yes","0.138","-0.316","0.591","0.55","0.148","-0.271","0.807","0.078","-0.362","0.518","0.729","0.081","-0.304","0.678"
|
||||
"16","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month","Six month"
|
||||
"17","active_treat.yes","-0.084","-0.249","0.081","0.316","-0.081","-0.22","0.084","-0.068","-0.237","0.1","0.426","-0.066","-0.211","0.105"
|
||||
"18","afli.yes","-0.025","-0.255","0.206","0.835","-0.024","-0.225","0.229","-0.063","-0.3","0.175","0.605","-0.061","-0.259","0.191"
|
||||
"19","age","0.001","-0.005","0.008","0.658","0.001","-0.005","0.008","-0.007","-0.015","0","0.062","-0.007","-0.015","0"
|
||||
"20","ami.yes","0.2","-0.103","0.502","0.195","0.221","-0.098","0.652","0.303","-0.031","0.638","0.076","0.354","-0.031","0.893"
|
||||
"21","any_reperf.yes","-0.028","-0.198","0.142","0.749","-0.027","-0.179","0.153","-0.035","-0.222","0.153","0.715","-0.034","-0.199","0.165"
|
||||
"22","cohab.yes","-0.094","-0.269","0.08","0.29","-0.09","-0.236","0.084","-0.008","-0.197","0.181","0.935","-0.008","-0.179","0.199"
|
||||
"23","diabetes.yes","0.012","-0.26","0.284","0.932","0.012","-0.229","0.329","-0.017","-0.299","0.265","0.905","-0.017","-0.259","0.303"
|
||||
"24","ever_smoker.yes","-0.021","-0.197","0.155","0.817","-0.02","-0.178","0.168","-0.001","-0.183","0.182","0.994","-0.001","-0.167","0.199"
|
||||
"25","female.yes","0.246","0.074","0.418","0.005","0.279","0.077","0.519","0.264","0.077","0.451","0.006","0.302","0.08","0.57"
|
||||
"26","hypertension.yes","0.232","0.068","0.396","0.006","0.261","0.07","0.487","0.1","-0.078","0.278","0.272","0.105","-0.075","0.321"
|
||||
"27","nihss_0","0.142","0.024","0.26","0.018","0.142","0.024","0.26","0.134","0.007","0.26","0.039","0.134","0.007","0.26"
|
||||
"28","pad.yes","0.416","0.001","0.831","0.05","0.516","0.001","1.296","0.304","-0.155","0.762","0.194","0.355","-0.143","1.143"
|
||||
"29","pase_0","-0.001","-0.003","0","0.002","-0.001","-0.003","0","-0.001","-0.003","0","0.017","-0.001","-0.003","0"
|
||||
"30","tci.yes","-0.66","-1.18","-0.14","0.013","-0.483","-0.693","-0.13","-0.695","-1.21","-0.18","0.008","-0.501","-0.702","-0.165"
|
||||
|
@ -1,34 +0,0 @@
|
||||
## =============================================================================
|
||||
##
|
||||
## Data modification and enriching
|
||||
##
|
||||
## =============================================================================
|
||||
|
||||
|
||||
## =============================================================================
|
||||
## Requirements
|
||||
## =============================================================================
|
||||
|
||||
# library(gtsummary)
|
||||
library(REDCapR)
|
||||
|
||||
## =============================================================================
|
||||
## Import
|
||||
## =============================================================================
|
||||
|
||||
token_talos<-read.csv("/Users/au301842/talos_redcap_token.csv",colClasses = "character")|>
|
||||
names()|>
|
||||
(\(x){ ## Shorthand for "anonymous lambda function"
|
||||
substr(x,2,33)})()|>
|
||||
suppressWarnings()
|
||||
|
||||
dta <- redcap_read_oneshot(
|
||||
redcap_uri = "https://redcap.au.dk/api/",
|
||||
token = token_talos
|
||||
)$data|>
|
||||
select(-c("cpr"))
|
||||
|
||||
## =============================================================================
|
||||
## MDI scores
|
||||
## =============================================================================
|
||||
|
||||
112
data_format.R
112
data_format.R
@ -1,112 +0,0 @@
|
||||
|
||||
library(REDCapR)
|
||||
library(gtsummary)
|
||||
theme_gtsummary_compact()
|
||||
library(REDCapR)
|
||||
library(gt)
|
||||
|
||||
library(lubridate)
|
||||
library(dplyr)
|
||||
library(tidyr)
|
||||
|
||||
|
||||
dta_all<-read.csv("/Volumes/Data/depression/dep_dataset.csv")
|
||||
|
||||
|
||||
# Defining patients to include for analysis
|
||||
# Only including cases with complete pase_0 and MDI at 1 & 6 months
|
||||
|
||||
dta<-dta_all[!is.na(dta_all$pase_0),]
|
||||
# &!is.na(dta$mdi_1)&!is.na(dta$mdi_6)
|
||||
|
||||
|
||||
## Formatting
|
||||
dta$diabetes<-factor(dta$diabetes)
|
||||
dta$pad<-factor(dta$pad)
|
||||
|
||||
dta$cohab<-ifelse(dta$civil=="partner","yes","no")|>
|
||||
factor()
|
||||
|
||||
dta$hypertension<-factor(dta$hypertension)
|
||||
|
||||
dta$afli[dta$afli=="unknown"]<-NA
|
||||
dta$afli<-factor(dta$afli)
|
||||
|
||||
dta$ever_smoker<-ifelse(dta$smoke_ever=="ever","yes","no")|>
|
||||
factor()
|
||||
|
||||
dta$ami<-factor(dta$ami)
|
||||
dta$tci<-factor(dta$tci)
|
||||
dta$thrombolysis<-factor(dta$thrombolysis)
|
||||
dta$thrombechtomy<-factor(dta$thrombechtomy)
|
||||
|
||||
dta$any_reperf<-ifelse(dta$rep_any=="rep","yes","no")|>
|
||||
factor()
|
||||
|
||||
dta$pad<-factor(dta$pad)
|
||||
dta$nihss_0<-as.numeric(dta$nihss_0)
|
||||
dta$age<-as.numeric(dta$age)
|
||||
|
||||
dta$active_treat<-ifelse(dta$rtreat=="Active","yes","no")|>
|
||||
factor()
|
||||
# dta$rtreat<-factor(dta$rtreat)
|
||||
|
||||
dta$female<-ifelse(dta$sex=="female","yes","no")|>
|
||||
factor()
|
||||
|
||||
dta$pase_0<-as.numeric(dta$pase_0)
|
||||
dta$pase_6<-as.numeric(dta$pase_6)
|
||||
dta$bmi<-as.numeric(dta$bmi)
|
||||
dta$mdi_6<-as.numeric(dta$mdi_6)
|
||||
dta$pase_0_bin<-factor(dta$pase_0_bin,levels=c("lower","higher"))
|
||||
|
||||
dta$nihss_0_isna<-is.na(dta$nihss_0)
|
||||
|
||||
|
||||
|
||||
vars<-c("pase_0",
|
||||
"female",
|
||||
"age",
|
||||
"cohab",
|
||||
"ever_smoker",
|
||||
"diabetes",
|
||||
"hypertension",
|
||||
"afli",
|
||||
"ami",
|
||||
"tci",
|
||||
"pad",
|
||||
"nihss_0",
|
||||
"any_reperf")
|
||||
|
||||
# tbl1_vars<-c("thrombolysis", "thrombechtomy","inc_time")
|
||||
|
||||
labels_all<-list(active_treat~"Active trial treatment",
|
||||
pase_0~"PASE score",
|
||||
age~"Age",
|
||||
female~"Female sex",
|
||||
ever_smoker~"History of smoking",
|
||||
cohab~"Cohabitation",
|
||||
diabetes~"Known diabetes",
|
||||
hypertension~"Known hypertension",
|
||||
afli~"Known Atrialfibrillation",
|
||||
ami~"Previos myocardial infarction",
|
||||
tci~"Previos TIA",
|
||||
pad~"Known peripheral artery disease",
|
||||
nihss_0~"NIHSS score",
|
||||
thrombolysis~"Thrombolytic therapy",
|
||||
thrombechtomy~"Endovascular treatment",
|
||||
any_reperf~"Any reperfusion treatment",
|
||||
inc_time~"Study inclusion time",
|
||||
'[Intercept]'~"Intercept")
|
||||
|
||||
lab_sel<-function(label_list,variables_vector){
|
||||
## Helper function to select labels for gtsummary function from list of all labels based on selected variables.
|
||||
## Long names in try to ease reading.
|
||||
include_index<-c()
|
||||
for (i in 1:length(label_list)) {
|
||||
include_index[i]<-as.character(label_list[[i]])[2] %in% variables_vector
|
||||
}
|
||||
return(label_list[include_index])
|
||||
}
|
||||
|
||||
dta_backup<-dta
|
||||
BIN
dep_data.pdf
Normal file
BIN
dep_data.pdf
Normal file
Binary file not shown.
BIN
dep_flow.pdf
Normal file
BIN
dep_flow.pdf
Normal file
Binary file not shown.
BIN
dep_imputation.pdf
Normal file
BIN
dep_imputation.pdf
Normal file
Binary file not shown.
BIN
dep_median.pdf
Normal file
BIN
dep_median.pdf
Normal file
Binary file not shown.
BIN
dep_regression.pdf
Normal file
BIN
dep_regression.pdf
Normal file
Binary file not shown.
BIN
dep_tableone.pdf
Normal file
BIN
dep_tableone.pdf
Normal file
Binary file not shown.
BIN
dep_tableone_enriched.pdf
Normal file
BIN
dep_tableone_enriched.pdf
Normal file
Binary file not shown.
@ -1,138 +0,0 @@
|
||||
back_trans <- function(gtt,outm=NULL,sqrts=NULL,logs=NULL,log1ps=NULL){
|
||||
## Handles back-transformation for non-strat and strat
|
||||
##
|
||||
## gtt = gt table element
|
||||
## outm = outcome meassure trans. One of c("log","log1p","sqrt"). The reciprocal function is used to back transform.
|
||||
## sqrts = sqrt-trasformed variable names
|
||||
## logs= log-trasformed variable names
|
||||
## log1ps= log1p-trasformed variable names
|
||||
|
||||
# gtt<-mul
|
||||
# outm<-"log1p"
|
||||
# sqrts <- "pase_0"
|
||||
# logs <- "nihss_0"
|
||||
|
||||
ests<-colnames(dplyr::select(gtt$table_body,contains("estimate")))
|
||||
lows<-colnames(dplyr::select(gtt$table_body,contains("conf.low")))
|
||||
highs<-colnames(dplyr::select(gtt$table_body,contains("conf.high")))
|
||||
|
||||
sqrt_ci <- function(gtt,all.v=FALSE,var=NULL){
|
||||
# Sqrt-transformation function to account for negative coefficients.
|
||||
if (all.v){
|
||||
var<-gtt$table_body$variable
|
||||
}
|
||||
|
||||
# There is a
|
||||
|
||||
for (i in var){
|
||||
for (j in ests){
|
||||
est<-gtt$table_body[gtt$table_body$variable==i,][j]
|
||||
|
||||
if (0 > est){
|
||||
gtt$table_body[gtt$table_body$variable==i,][j]<- (0-(est^2))
|
||||
} else {
|
||||
gtt$table_body[gtt$table_body$variable==i,][j]<- (est^2)
|
||||
}
|
||||
}
|
||||
|
||||
for (j in lows){
|
||||
low<-gtt$table_body[gtt$table_body$variable==i,][j]
|
||||
|
||||
if (0 > low){
|
||||
gtt$table_body[gtt$table_body$variable==i,][j]<- (0-(low^2))
|
||||
} else {
|
||||
gtt$table_body[gtt$table_body$variable==i,][j]<- (low^2)
|
||||
}
|
||||
}
|
||||
|
||||
for (j in highs){
|
||||
high<-gtt$table_body[gtt$table_body$variable==i,][j]
|
||||
|
||||
if (0 > high){
|
||||
gtt$table_body[gtt$table_body$variable==i,][j]<- (0-(high^2))
|
||||
} else {
|
||||
gtt$table_body[gtt$table_body$variable==i,][j]<- (high^2)
|
||||
}
|
||||
}
|
||||
}
|
||||
return(gtt)
|
||||
}
|
||||
|
||||
log_ci <- function(gtt,all.v=FALSE,var=NULL){
|
||||
# Log-transformation function
|
||||
if (all.v){
|
||||
var<-gtt$table_body$variable
|
||||
}
|
||||
|
||||
for (i in var){
|
||||
for (j in ests){
|
||||
gtt$table_body[gtt$table_body$variable==i,][j]<-
|
||||
exp(gtt$table_body[gtt$table_body$variable==i,][j])
|
||||
}
|
||||
|
||||
for (j in lows){
|
||||
gtt$table_body[gtt$table_body$variable==i,][j]<-
|
||||
exp(gtt$table_body[gtt$table_body$variable==i,][j])
|
||||
|
||||
}
|
||||
|
||||
for (j in highs){
|
||||
gtt$table_body[gtt$table_body$variable==i,][j]<-
|
||||
exp(gtt$table_body[gtt$table_body$variable==i,][j])
|
||||
}
|
||||
}
|
||||
return(gtt)
|
||||
}
|
||||
|
||||
log1p_ci <- function(gtt,all.v=FALSE,var=NULL){
|
||||
# Log1p-transformation function
|
||||
if (all.v){
|
||||
var<-gtt$table_body$variable
|
||||
}
|
||||
|
||||
for (i in var){
|
||||
for (j in ests){
|
||||
gtt$table_body[gtt$table_body$variable==i,][j]<-
|
||||
expm1(gtt$table_body[gtt$table_body$variable==i,][j])
|
||||
}
|
||||
|
||||
for (j in lows){
|
||||
gtt$table_body[gtt$table_body$variable==i,][j]<-
|
||||
expm1(gtt$table_body[gtt$table_body$variable==i,][j])
|
||||
|
||||
}
|
||||
|
||||
for (j in highs){
|
||||
gtt$table_body[gtt$table_body$variable==i,][j]<-
|
||||
expm1(gtt$table_body[gtt$table_body$variable==i,][j])
|
||||
}
|
||||
}
|
||||
return(gtt)
|
||||
}
|
||||
|
||||
if (outm=="log1p"){
|
||||
## Transforming log1p() to expm1()
|
||||
gtt<-log1p_ci(gtt,all.v=TRUE)
|
||||
}
|
||||
|
||||
if (outm=="log"){
|
||||
## Transforming log1p() to expm1()
|
||||
gtt<-log_ci(gtt,all.v=TRUE)
|
||||
}
|
||||
|
||||
if (outm=="sqrt"){
|
||||
gtt<-sqrt_ci(gtt,all.v=TRUE)
|
||||
}
|
||||
|
||||
# Sqrts
|
||||
gtt<-sqrt_ci(gtt,var=sqrts)
|
||||
|
||||
# Log1ps
|
||||
gtt<-log1p_ci(gtt, var=log1ps)
|
||||
|
||||
# Logs
|
||||
gtt<-log_ci(gtt, var=logs)
|
||||
|
||||
|
||||
return(gtt)
|
||||
}
|
||||
@ -1,29 +0,0 @@
|
||||
reg_fun <- function(outs=c("mdi_1_enr","mdi_6_newobs_enr"),
|
||||
lbl_x=NULL,
|
||||
strat_var=NULL,
|
||||
trans_vars=TRUE,
|
||||
sqrt_vars="pase_0",
|
||||
log1p_vars="nihss_0",
|
||||
log_vars=NULL,
|
||||
trans_back=TRUE,
|
||||
print_tbl=TRUE){
|
||||
|
||||
##
|
||||
## THIS IS NOT WORKING. WISH IT WOULD!
|
||||
##
|
||||
|
||||
# outs Outcoume variables
|
||||
# lbl_x # Extra label in file name,
|
||||
# strat_var # Variable to stratify by. Only one variable(!),
|
||||
# trans_vars # Transform variables? T/F,
|
||||
# sqrt_vars" # Variables to sqrt-transfom,
|
||||
# log1p_vars" # Variables to log1p-transform, not outcome,
|
||||
# log_vars # Variables to log-transform,
|
||||
# log1p_vars_all # All variables to log1p-transform, incl outcome,
|
||||
# trans_back # Back transform variables? T/F,
|
||||
# print_tbl # Print tables? T/F){
|
||||
|
||||
log1p_vars_all <- c(log1p_vars,outs)
|
||||
source("data_format.R")
|
||||
source("biv_mul.R")
|
||||
}
|
||||
@ -1,87 +0,0 @@
|
||||
reg_table <- function(X,y,m.biv=TRUE,m.mul=TRUE,trans.out=FALSE,trans.var=FALSE,outm=NULL,sqrt.vars=NULL,log1p.vars=NULL,inter.add=NULL){
|
||||
# method One of biv, mul, biv_mul
|
||||
|
||||
source("function_trans_table.R")
|
||||
|
||||
cols<-c("name","pred", "co", "lo", "hi", "pv")
|
||||
|
||||
if(!is.null(inter.add)){
|
||||
form_add<-paste0(paste0(inter.add,collapse = "*"),"+")
|
||||
m.biv <- FALSE # If interaction term is added, only multivariate is performed.
|
||||
} else {
|
||||
form_add=NULL
|
||||
}
|
||||
|
||||
if (m.biv){
|
||||
df_b <- data.frame(matrix(NA, ncol = length(cols)))
|
||||
names(df_b)<-cols
|
||||
|
||||
|
||||
for (j in colnames(X)){
|
||||
m<-lm(formula(paste0("y~",j)),X)
|
||||
|
||||
ci <- confint(m)
|
||||
lo <- ci[-1, 1]
|
||||
hi <- ci[-1, 2]
|
||||
co <- coef(m)[-1]
|
||||
#pv <- broom::tidy(m)$p.value[-1]
|
||||
pv <- summary(m)$coefficients[2,4] # Avoids dependency
|
||||
x1 <- X[, j]
|
||||
if (is.factor(x1)) {
|
||||
pred <- paste(j, levels(x1)[-1],
|
||||
sep = ".")
|
||||
} else { pred <- j }
|
||||
|
||||
df_b <- rbind(df_b, cbind(name=j, pred, co, lo, hi, pv))
|
||||
|
||||
}
|
||||
|
||||
df_b <- df_b[-1,]
|
||||
if (trans.var){
|
||||
df_b <- trans_table(df_b,sqrts=sqrt.vars,f.vars=f.names)
|
||||
}
|
||||
|
||||
df_b<-df_b|>data.frame()|>mutate(across(matches('co|lo|hi'),as.numeric))
|
||||
}
|
||||
|
||||
if (m.mul){
|
||||
m<-lm(formula(paste0("y~",form_add,".")),X)
|
||||
|
||||
ci <- confint(m)
|
||||
lo <- ci[-1, 1]
|
||||
hi <- ci[-1, 2]
|
||||
co <- coef(m)[-1]
|
||||
#pv <- broom::tidy(m)$p.value[-1]
|
||||
pv <- summary(m)$coefficients[-1,4] # Avoids dependency
|
||||
pred <- c()
|
||||
for (j in colnames(X)){
|
||||
x1 <- X[, j]
|
||||
if (is.factor(x1)) {
|
||||
pred <- c(pred,
|
||||
paste(j, levels(x1)[-1],
|
||||
sep = "."))
|
||||
} else { pred <- c(pred,
|
||||
j) }
|
||||
}
|
||||
df_m <- cbind(name=c(colnames(X),form_add), pred=c(pred,form_add), co, lo, hi, pv)
|
||||
|
||||
if (trans.var){
|
||||
df_m <- trans_table(df_m,sqrts=sqrt.vars,f.vars=f.names)
|
||||
}
|
||||
|
||||
df_m<-df_m|>data.frame()|>mutate(across(matches('co|lo|hi'),as.numeric))
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
if (all(m.biv,m.mul)){
|
||||
colnames(df_b)[3:ncol(df_b)]<-paste0("biv_",colnames(df_b)[3:ncol(df_b)])
|
||||
colnames(df_m)[3:ncol(df_m)]<-paste0("mul_",colnames(df_m)[3:ncol(df_m)])
|
||||
|
||||
return(merge(df_b,df_m,by=c("name","pred"),sort=FALSE))
|
||||
} else if (m.biv) {
|
||||
return(df_b)
|
||||
} else {return(df_m)}
|
||||
|
||||
}
|
||||
@ -1,25 +0,0 @@
|
||||
trans_cols<-function(ds,sqrts=NULL,logs=NULL,log1ps=NULL){
|
||||
## gtt = gt table element
|
||||
## sqrts = sqrt-trasformed variable names
|
||||
## logs= log-trasformed variable names
|
||||
## log1ps= log1p-trasformed variable names
|
||||
|
||||
# gtt<-mul
|
||||
# outm<-"log1p"
|
||||
# sqrts <- "pase_0"
|
||||
# logs <- "nihss_0"
|
||||
|
||||
for (i in sqrts){
|
||||
ds[i]<-sqrt(ds[i])
|
||||
}
|
||||
|
||||
for (i in logs){
|
||||
ds[i]<-log(ds[i])
|
||||
}
|
||||
|
||||
for (i in log1ps){
|
||||
ds[i]<-log1p(ds[i])
|
||||
}
|
||||
|
||||
return(ds)
|
||||
}
|
||||
@ -1,55 +0,0 @@
|
||||
trans_table <- function(ds,f.vars,sqrts=NULL,logs=NULL){
|
||||
## Handles back-transformation for table with co, lo and hi
|
||||
##
|
||||
## ds = matrix or table including colnames co, lo and hi
|
||||
## sqrts sqrt-trasformed variables names
|
||||
## logs log-trasformed independent variables names
|
||||
## f.vars dichotomous variables
|
||||
# f.vars <- f.names
|
||||
# ds<-df_m
|
||||
# sqrts <- "pase_0"
|
||||
# logs <- "nihss_0"
|
||||
|
||||
ds<-ds|>data.frame()|>mutate(across(c("co", "lo", "hi"),as.numeric))
|
||||
|
||||
for (i in sqrts){
|
||||
est<-ds$co[ds$name==i]
|
||||
if (0 > est){
|
||||
ds$co[ds$name==i]<- (0-(est^2))
|
||||
} else {
|
||||
ds$co[ds$name==i]<- (est^2)
|
||||
}
|
||||
|
||||
low<-ds$lo[ds$name==i]
|
||||
if (0 > low){
|
||||
ds$lo[ds$name==i]<- (0-(low^2))
|
||||
} else {
|
||||
ds$lo[ds$name==i]<- (low^2)
|
||||
}
|
||||
|
||||
high<-ds$hi[ds$name==i]
|
||||
if (0 > high){
|
||||
ds$hi[ds$name==i]<- (0-(high^2))
|
||||
} else {
|
||||
ds$hi[ds$name==i]<- (high^2)
|
||||
}
|
||||
}
|
||||
|
||||
cols<-c("name","co.p", "lo.p", "hi.p")
|
||||
|
||||
ds_r <- data.frame(matrix(ncol = length(cols)))
|
||||
names(ds_r)<-cols
|
||||
|
||||
for (j in 1:nrow(ds)){
|
||||
if (ds$name[j]%in%f.vars){
|
||||
ds_r<-rbind(ds_r,c(name=ds$name[j],sapply(ds[j,3:5],expm1)))
|
||||
} else {
|
||||
ds_rn <- c(ds$name[j],ds[j,3:5])
|
||||
names(ds_rn) <- cols
|
||||
ds_r<-rbind(ds_r,ds_rn)
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
return(merge(ds,ds_r,by="name",sort = FALSE))
|
||||
}
|
||||
@ -1,97 +0,0 @@
|
||||
## =============================================================================
|
||||
## Requirements
|
||||
## =============================================================================
|
||||
|
||||
source("function_trans_cols.R")
|
||||
|
||||
if (trans_vars==TRUE){
|
||||
# If trans_vars flag is TRUE, transform specified variables
|
||||
dta<-trans_cols(dta_backup,sqrts=sqrt_vars,log1ps = log1p_vars_all)
|
||||
} else {dta<-dta_backup}
|
||||
|
||||
library(dplyr)
|
||||
library(gtsummary)
|
||||
|
||||
source("function_back_trans.R")
|
||||
|
||||
## =============================================================================
|
||||
## Loop
|
||||
## =============================================================================
|
||||
|
||||
bm_list<-list()
|
||||
|
||||
for (i in 1:length(outs)){
|
||||
|
||||
dta_l<-dta|>
|
||||
dplyr::select(all_of(c("active_treat",vars,outs[i]))) # active_treat should be vector
|
||||
|
||||
sel<-dta_l|>
|
||||
sapply(is.factor)
|
||||
|
||||
## Multivariate
|
||||
mul<-dta_l |>
|
||||
lm(formula(
|
||||
paste(c(paste(c(outs[i],paste(c("active_treat","pase_0"),collapse="*")),collapse="~"),"."),collapse="+")),
|
||||
data = _)|>
|
||||
tbl_regression(label = lab_sel(labels_all,c(vars,"active_treat")),
|
||||
show_single_row=colnames(dta_l)[sel],
|
||||
estimate_fun = ~style_sigfig(.x,digits = 3),
|
||||
pvalue_fun = ~style_pvalue(.x, digits = 3))|>
|
||||
add_n() |>
|
||||
add_global_p() |>
|
||||
bold_p() |>
|
||||
bold_labels() |>
|
||||
italicize_levels()
|
||||
|
||||
|
||||
mul <- back_trans(mul, outm = "log1p" ,sqrts = "pase_0",log1ps = "nihss_0")
|
||||
|
||||
|
||||
bm_list[[i]]<-mul
|
||||
}
|
||||
|
||||
## =============================================================================
|
||||
## Big merge
|
||||
## =============================================================================
|
||||
|
||||
bm_16_tbl <- tbl_merge(
|
||||
tbls = bm_list,
|
||||
tab_spanner = c("**One month follow up**",
|
||||
"**Six months follow up**")
|
||||
)
|
||||
|
||||
|
||||
|
||||
library(aod)
|
||||
|
||||
for (i in 1:length(outs)){
|
||||
model<-dta |>
|
||||
dplyr::select(all_of(c("active_treat",vars,outs[i])))|>
|
||||
lm(formula(
|
||||
paste(c(paste(c(outs[i],paste(c("active_treat","pase_0"),collapse="*")),collapse="~"),"."),collapse="+")),
|
||||
data = _)
|
||||
|
||||
wt<-wald.test(Sigma = vcov(model),
|
||||
b = coef(model),
|
||||
Terms = model$rank # Rank gives number of coefficients. The interaction is the last.
|
||||
)
|
||||
print(wt)
|
||||
}
|
||||
|
||||
## =============================================================================
|
||||
## Print
|
||||
## =============================================================================
|
||||
|
||||
# File name depending onstratification, transformation and back transformation
|
||||
if (print_tbl==TRUE){
|
||||
fnm<-paste0("bm_16_tbl",lbl_x)
|
||||
if (!is.null(strat_var)){fnm<-paste0(fnm,"_strat")}
|
||||
if (trans_vars==TRUE){fnm<-paste0(fnm,"_trans")}
|
||||
if (trans_back==TRUE){fnm<-paste0(fnm,"_back")}
|
||||
|
||||
|
||||
bm_16_tbl_rtf <- file(paste0(fnm,".RTF"), "w")
|
||||
writeLines(bm_16_tbl%>%as_gt()%>%as_rtf(), bm_16_tbl_rtf)
|
||||
close(bm_16_tbl_rtf)
|
||||
|
||||
}
|
||||
@ -1,91 +0,0 @@
|
||||
## =============================================================================
|
||||
## Requirements
|
||||
## =============================================================================
|
||||
|
||||
source("function_trans_cols.R")
|
||||
|
||||
if (trans_vars==TRUE){
|
||||
# If trans_vars flag is TRUE, transform specified variables
|
||||
dta<-trans_cols(dta_backup,sqrts=sqrt_vars,log1ps = log1p_vars_all)
|
||||
} else {dta<-dta_backup}
|
||||
|
||||
library(dplyr)
|
||||
library(gtsummary)
|
||||
|
||||
source("function_back_trans.R")
|
||||
|
||||
## =============================================================================
|
||||
## Regression tables
|
||||
## =============================================================================
|
||||
|
||||
dta_l<-dta|>
|
||||
dplyr::select(all_of(c(vars,outs))) # active_treat should be vector
|
||||
|
||||
sel<-dta_l|>
|
||||
sapply(is.factor)
|
||||
|
||||
## Bivariate
|
||||
biv<-dta_l|>
|
||||
tbl_uvregression(data=_,
|
||||
y=outs,
|
||||
method=lm,
|
||||
label = lab_sel(labels_all,vars),
|
||||
show_single_row=colnames(dta_l)[sel],
|
||||
estimate_fun = ~style_sigfig(.x,digits = 3),
|
||||
pvalue_fun = ~style_pvalue(.x, digits = 3)
|
||||
) |>
|
||||
add_global_p()|>
|
||||
bold_p()
|
||||
|
||||
## Multivariate
|
||||
mul<-dta_l |>
|
||||
lm(nihss_0~pase_0+.,
|
||||
data = _) |>
|
||||
tbl_regression(show_single_row=colnames(dta_l)[sel],
|
||||
estimate_fun = ~style_sigfig(.x,digits = 3),
|
||||
pvalue_fun = ~style_pvalue(.x, digits = 3),
|
||||
label = lab_sel(labels_all,vars)
|
||||
)|>
|
||||
add_n() |>
|
||||
add_global_p() |>
|
||||
bold_p() |>
|
||||
bold_labels() |>
|
||||
italicize_levels()
|
||||
|
||||
# Back transforming if flag set
|
||||
if (trans_back==TRUE){
|
||||
ls<-lapply(list(biv,mul), back_trans, outm = "log1p" ,sqrts = sqrt_vars)
|
||||
} else {ls<-list(biv,mul)}
|
||||
|
||||
|
||||
## =============================================================================
|
||||
## Merge
|
||||
## =============================================================================
|
||||
|
||||
if (trans_back==TRUE){tab_span<-c("**Bivariate linear regression [TRANS t/r]**",
|
||||
"**Multivariate linear regression [TRANS t/r]**")
|
||||
} else {tab_span<-c("**Bivariate linear regression**",
|
||||
"**Multivariate linear regression**")}
|
||||
|
||||
bm_16_tbl<-tbl_merge(
|
||||
tbls = ls,
|
||||
tab_spanner = tab_span
|
||||
)
|
||||
|
||||
## =============================================================================
|
||||
## Print
|
||||
## =============================================================================
|
||||
|
||||
# File name depending onstratification, transformation and back transformation
|
||||
if (print_tbl==TRUE){
|
||||
fnm<-paste0("bm_16_tbl",lbl_x)
|
||||
if (!is.null(strat_var)){fnm<-paste0(fnm,"_strat")}
|
||||
if (trans_vars==TRUE){fnm<-paste0(fnm,"_trans")}
|
||||
if (trans_back==TRUE){fnm<-paste0(fnm,"_back")}
|
||||
|
||||
|
||||
bm_16_tbl_rtf <- file(paste0(fnm,".RTF"), "w")
|
||||
writeLines(bm_16_tbl%>%as_gt()%>%as_rtf(), bm_16_tbl_rtf)
|
||||
close(bm_16_tbl_rtf)
|
||||
|
||||
}
|
||||
15
table_1.R
15
table_1.R
@ -1,15 +0,0 @@
|
||||
tbl1_vars<-c("active_treat",vars,"inc_time")
|
||||
|
||||
tbl_1<-dta|>
|
||||
tbl_summary(missing = "ifany",
|
||||
include = all_of(tbl1_vars),
|
||||
missing_text="(Missing)",
|
||||
label = lab_sel(labels_all,tbl1_vars)
|
||||
)|>
|
||||
add_n()|>
|
||||
as_gt() |>
|
||||
# modify with gt functions
|
||||
gt::tab_header("Baseline Characteristics") |>
|
||||
gt::tab_options(
|
||||
table.font.size = "small",
|
||||
data_row.padding = gt::px(1))
|
||||
Loading…
Reference in New Issue
Block a user