300 lines
7.4 KiB
Plaintext
300 lines
7.4 KiB
Plaintext
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---
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title: "Sensitivity analysis on imputed dataset"
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author: "Andreas Gammelgaard Damsbo"
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date: "Knitted: `r format(Sys.time(), '%d %B, %Y')`"
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output: pdf_document
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---
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```{r setup, include=FALSE}
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knitr::opts_chunk$set(echo = TRUE, message = FALSE)
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```
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# Import
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```{r}
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dta_all<-read.csv("/Volumes/Data/depression/dep_dataset.csv",na.strings = c("NA","unknown")) ## Extending definition of NA's for imputation
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```
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# Defining patients to include for analysis
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Only including cases with complete pase_0 and MDI at 1 & 6 months
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```{r}
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dta<-dta_all[!is.na(dta_all$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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dta$civil<-factor(dta$civil)
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dta$hypertension<-factor(dta$hypertension)
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dta$afli<-factor(dta$afli)
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dta$smoke_ever<-factor(dta$smoke_ever)
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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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dta$rep_any<-factor(dta$rep_any)
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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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dta$rtreat<-factor(dta$rtreat)
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dta$sex<-factor(dta$sex)
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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"))
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```
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```{r}
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# Backup
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dta_b<-dta
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```
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# Libraries
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```{r}
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library(daDoctoR)
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library(mice)
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```
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## Variables to include in imputation
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```{r}
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# Possible variables to include
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coval<-c("pase_0_bin","rtreat","age","sex","smoke_ever","civil","bmi","diabetes", "hypertension", "afli","pad","nihss_0","rep_any")
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```
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# Imputation
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```{r}
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# Output variables added to include in model. Excluded from predicting.
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outc<-c("mdi_1_enr","mdi_6_newobs_enr")
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# Adding all
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covar<-c(coval,outc)
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# Selecting dataset
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r<-dta[,c("rnumb",covar)]
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```
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```{r}
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# Iterations
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mxt=20
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# Imputations
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mis=5
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```
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https://datascienceplus.com/imputing-missing-data-with-r-mice-package/
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```{r}
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md.pattern(r) # Missing pattern
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# library(VIM)
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# aggr_plot <- aggr(r, col=c('navyblue','red'), numbers=TRUE, sortVars=TRUE, labels=names(data), cex.axis=.7, gap=3, ylab=c("Histogram of missing data","Pattern"))
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```
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```{r}
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init <- mice(r, maxit=0) # Creating initial imputation list to assess methods
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meth <- init$method
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meth
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predM <- init$predictorMatrix
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predM[, c("rnumb")] <- 0 # Defining variables not to be used for predicting imputed values
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# meth[outc]=""
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# Defining variables not to be imputed.
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# Commented out as all included variables will be imputed
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```
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```{r echo=FALSE}
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imputed <- mice(r, method=meth, predictorMatrix=predM, m=mis, maxit = mxt,seed = 103, printFlag=FALSE)
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```
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```{r}
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# summary(imputed)
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```
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```{r}
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library(dplyr)
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export<-dta %>%
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select(-all_of(coval)) %>% # Leaving out imputed variables from original dataset
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left_join(mice::complete(imputed,1),.,by="rnumb") # Join with the first imputed dataset for a full dataset export
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md.pattern(export[coval]) # Ensuring complete data
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write.csv(export,"/Volumes/Data/depression/imputed.csv",row.names = FALSE) # Export
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```
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# Regression analyses
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```{r}
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print(adjs_10<-rep_lm(meas = "mdi_6",string=c("pase_0_bin","rtreat",coval),data=dta,cut.p = .1)[[2]])
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```
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## Bivariabel
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Function to format collected data from pool function
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```{r}
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pool_table<-function(clls){
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## Variables needed: estimate, p.value, term
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coll$lo<-round(coll$estimate-coll$std.error*1.96,2)
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coll$hi<-round(coll$estimate+coll$std.error*1.96,2)
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pa<-coll$p.value
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pa <- ifelse(pa < 0.001, "<0.001", round(pa, 3))
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pa <- ifelse(pa <= 0.05 | pa == "<0.001", paste0("*",pa), ifelse(pa > 0.05 & pa <= 0.1, paste0(".", pa),pa))
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cl<-data.frame(id=coll$term,diff=paste0(round(coll$estimate,2)," (",coll$lo," to ",coll$hi,")"),p=pa,stringsAsFactors=FALSE)
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return(cl)
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}
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keeps<-c("term","estimate","std.error","p.value")
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```
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### Repeated bivariabel analysis
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Not necessary for this, but an interesting addition
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```{r}
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coll<-c()
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for (i in c("rtreat",adjs_10)){
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## Bivariable linear regression analysis of all
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coeffs<-summary(pool(
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with(imputed,lm(as.formula(paste0("mdi_1_enr~",i))))
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))[-1,c("term","estimate","std.error","p.value")]
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coll<-rbind(coll,coeffs)
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## Inspiration: https://stackoverflow.com/questions/40132829/r-for-loop-in-a-formula
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## Also: https://gist.github.com/AaronGullickson/3ccb3fdd1778b32fc46df40d78faf5d3
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}
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## Collecting
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coll$lo<-round(coll$estimate-coll$std.error*1.96,2)
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coll$hi<-round(coll$estimate+coll$std.error*1.96,2)
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pa<-coll$p.value
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pa <- ifelse(pa < 0.001, "<0.001", round(pa, 3))
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pa <- ifelse(pa <= 0.05 | pa == "<0.001", paste0("*",pa), ifelse(pa > 0.05 & pa <= 0.1, paste0(".", pa),pa))
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coll_bi<-data.frame(diff=paste0(round(exp(coll$estimate),2)," (",coll$lo," to ",coll$hi,")"),p=pa,id=coll$term,stringsAsFactors=FALSE)
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```
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## Unadjusted analyses
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```{r}
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adjs_10m<-adjs_10[adjs_10!="pase_0_bin"]
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adj_m<-c("rtreat","pase_0_bin")
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coll<-c()
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nms<-c()
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for (l in outc){
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for (i in adj_m){
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coeffs<-summary(pool(
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with(imputed,lm(as.formula(paste0(l,"~",i))))
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))[-1,keeps]
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coll<-rbind(coll,coeffs)
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nms<-c(nms,paste(l,i,sep = "_"))
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d.long <- mice::complete(imputed,"long",include = T)
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# Inspiration: https://stackoverflow.com/questions/53014141/mice-splitting-imputed-data-for-further-analysis
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for (j in levels(d.long[[i]])){
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k<-length(adj_m)-grep(i,adj_m)+1 ## This only works to select the "opposite" of i for length(adj_m)==2
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s.imp<-mice::as.mids(d.long[which(d.long[[i]] == j),]) # Subsetting long and convert to "mids" format for pooling
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coeffs<-summary(pool(
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with(s.imp,lm(as.formula(paste0(l,"~",adj_m[k]))))
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))[-1,keeps]
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coll<-rbind(coll,coeffs)
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nms<-c(nms,paste(l,j,sep = "_"))
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}
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## Inspiration: https://stackoverflow.com/questions/40132829/r-for-loop-in-a-formula
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## Also: https://gist.github.com/AaronGullickson/3ccb3fdd1778b32fc46df40d78faf5d3
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}
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}
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coll$term<-nms
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```
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### Collecting
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```{r}
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biv_coll<-pool_table(coll)
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```
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## Adjusted analyses
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```{r}
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adjs_10m<-adjs_10[adjs_10!="pase_0_bin"]
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adj_m<-c("rtreat","pase_0_bin")
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coll<-c()
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nms<-c()
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for (l in outc){
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# l="mdi_1_enr"
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for (i in adj_m){
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coeffs<-summary(pool(
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with(imputed,lm(as.formula(paste0(l,"~",paste(i,paste(adjs_10m,collapse="+"),sep="+")))))
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))[2,keeps]
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coll<-rbind(coll,coeffs)
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nms<-c(nms,paste(l,i,sep = "_"))
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d.long <- mice::complete(imputed,"long",include = T)
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# Inspiration: https://stackoverflow.com/questions/53014141/mice-splitting-imputed-data-for-further-analysis
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for (j in levels(d.long[[i]])){
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k<-length(adj_m)-grep(i,adj_m)+1 ## This only works to select the "opposite" of i for length(adj_m)==2
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s.imp<-mice::as.mids(d.long[which(d.long[[i]] == j),]) # Subsetting long and convert to "mids" format for pooling
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coeffs<-summary(pool(
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with(s.imp,lm(as.formula(paste0(l,"~",paste(adj_m[k],paste(adjs_10m,collapse="+"),sep="+")))))
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))[2,keeps]
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coll<-rbind(coll,coeffs)
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nms<-c(nms,paste(l,j,sep = "_"))
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}
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## Inspiration: https://stackoverflow.com/questions/40132829/r-for-loop-in-a-formula
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## Also: https://gist.github.com/AaronGullickson/3ccb3fdd1778b32fc46df40d78faf5d3
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}
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}
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coll$term<-nms
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```
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### Collecting
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```{r}
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mul_coll<-pool_table(coll)
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colnames(mul_coll)[-1]<-paste0("adj_",colnames(mul_coll)[-1])
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```
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```{r}
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library(lubridate)
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write.csv(full_join(biv_coll,mul_coll),paste0("/Volumes/Data/depression/imp_regression_",today(),".csv"))
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```
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