updates

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: daDoctoR
 Type: Package
 Title: FUNCTIONS FOR HEALTH RESEARCH
-Version: 0.1.0.9024
+Version: 0.1.0.9025
 Author: c(person("Andreas", "Gammelgaard Damsbo", email = "agdamsbo@pm.me", role = c("cre", "aut")))
 Maintainer: Andreas Gammelgaard Damsbo <agdamsbo@pm.me>
 Description: I am a Danish medical doctor involved in neuropsychiatric research. 

R/strobe_diff_bygroup.R

@@ -3,7 +3,7 @@
 #' Printable table of three dimensional regression analysis of group vs var for meas. By group.
 #' @param meas outcome meassure variable name in data-data.frame as a string. Can be numeric or factor. Result is calculated accordingly.
 #' @param var binary exposure variable to compare against (active vs placebo). As string.
-#' @param groups groups to compare, as string.
+#' @param group group to compare, as string.
 #' @param adj variables to adjust for, as string.
 #' @param data dataframe of data.
 #' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1. pval has 3 decimals.

R/strobe_diff_byvar.R

@@ -3,7 +3,7 @@
 #' Printable table of three dimensional regression analysis of group vs var for meas. By var. Includes p-values.
 #' @param meas outcome meassure variable name in data-data.frame as a string. Can be numeric or factor. Result is calculated accordingly.
 #' @param var binary exposure variable to compare against (active vs placebo). As string.
-#' @param groups groups to compare, as string.
+#' @param group groups to compare, as string.
 #' @param adj variables to adjust for, as string.
 #' @param data dataframe of data.
 #' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1.

R/strobe_olr.R

@@ -1,6 +1,6 @@
-#' Print regression results according to STROBE
+#' Print ordinal logistic regression results according to STROBE
 #'
-#' Printable table of logistic regression analysis oaccording to STROBE.
+#' Printable table of ordinal logistic regression analysis oaccording to STROBE. Uses polr() funtion of the MASS-package.
 #' @param meas outcome meassure variable name in data-data.frame as a string. Can be numeric or factor. Result is calculated accordingly.
 #' @param vars variables to compare against. As vector of columnnames.
 #' @param data dataframe of data.

R/strobe_pred.R

@@ -1,15 +1,16 @@
 #' Regression model of predictors according to STROBE, bi- and multivariate.
 #'
-#' Printable table of regression model according to STROBE. Includes borth bivariate and multivariate in the same table. Output is a list, with the first item being the main "output" as a dataframe. Automatically uses logistic regression model for dichotomous outcome variable and linear regression model for continous outcome variable.
+#' Printable table of regression model according to STROBE. Includes borth bivariate and multivariate in the same table. Output is a list, with the first item being the main "output" as a dataframe. Automatically uses logistic regression model for dichotomous outcome variable and linear regression model for continous outcome variable. Linear regression will give estimated adjusted true mean in list.
 #' @param meas binary outcome meassure variable, column name in data.frame as a string. Can be numeric or factor. Result is calculated accordingly.
 #' @param adj variables to adjust for, as string.
 #' @param data dataframe of data.
 #' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1.
 #' @param n.by.adj flag to indicate wether to count number of patients in adjusted model or overall for outcome meassure not NA.
+#' @param p.val flag to include p-values in linear regression for now, set to FALSE as standard.
 #' @keywords logistic
 #' @export
 
-strobe_pred<-function(meas,adj,data,dec=2,n.by.adj=FALSE){
+strobe_pred<-function(meas,adj,data,dec=2,n.by.adj=FALSE,p.val=FALSE){
   ## Ønskeliste:
   ##
   ## - Tæl selv antal a NA'er
@@ -178,7 +179,7 @@ strobe_pred<-function(meas,adj,data,dec=2,n.by.adj=FALSE){
     ads<-d[,c(adj)]
 
     dfcr<-data.frame(matrix(NA,ncol = 3))
-    names(dfcr)<-c("pred","mean_ci","pv")
+    names(dfcr)<-c("pred","dif_ci","pv")
     n.mn<-c()
 
     nref<-c()
@@ -192,9 +193,13 @@ strobe_pred<-function(meas,adj,data,dec=2,n.by.adj=FALSE){
       suppressMessages(ci<-confint(mn))
       l<-round(ci[-1,1],dec)
       u<-round(ci[-1,2],dec)
-      mean<-round(coef(mn)[-1],dec)
+      dif<-round(coef(mn)[-1],dec)
-      mean_ci<-paste0(mean," (",l," to ",u,")")
+      dif_ci<-paste0(dif," (",l," to ",u,")")
       pv<-round(tidy(mn)$p.value[-1],dec+1)
+      pv<-ifelse(pv<0.001,"<0.001",round(pv,3))
+      pv <- ifelse(pv<=0.05|pv=="<0.001",paste0("*",pv),
+                   ifelse(pv>0.05&pv<=0.1,paste0(".",pv),pv))
+
       x1<-ads[,i]
 
       if (is.factor(x1)){
@@ -205,7 +210,7 @@ strobe_pred<-function(meas,adj,data,dec=2,n.by.adj=FALSE){
         pred<-names(ads)[i]
       }
 
-      dfcr<-rbind(dfcr,cbind(pred,mean_ci,pv))
+      dfcr<-rbind(dfcr,cbind(pred,dif_ci,pv))
     }
 
     ## Mutually adjusted  ORs
@@ -214,6 +219,7 @@ strobe_pred<-function(meas,adj,data,dec=2,n.by.adj=FALSE){
     ma <- lm(m ~ ., data = dat)
     miss<-length(ma$na.action)
 
+
     actable <- coef(summary(ma))
     pa <- actable[,4]
     pa<-ifelse(pa<0.001,"<0.001",round(pa,3))
@@ -228,6 +234,8 @@ strobe_pred<-function(meas,adj,data,dec=2,n.by.adj=FALSE){
     aup<-aci[,2]
     amean_ci<-paste0(aco," (",alo," to ",aup,")")
 
+    mean_est<-amean_ci[[1]]
+
 
     nq<-c()
 
@@ -312,12 +320,19 @@ strobe_pred<-function(meas,adj,data,dec=2,n.by.adj=FALSE){
 
     suppressWarnings(re<-left_join(df,dfcr,by="names"))
 
-    ref<-data.frame(re[,1],re[,2],re[,5],re[,3])
+    if (p.val==TRUE){
+      ref<-data.frame(re[,1],re[,2],re[,5],re[,6],re[,3],re[,4])
 
-    names(ref)<-c("Variable",paste0("N=",n.meas),"Crude OR (95 % CI)","Mutually adjusted OR (95 % CI)")
+      names(ref)<-c("Variable",paste0("N=",n.meas),"Difference (95 % CI)","p-value","Mutually adjusted difference (95 % CI)","A p-value")
+    }
+    else{
+      ref<-data.frame(re[,1],re[,2],re[,5],re[,3])
 
-    ls<-list(tbl=ref,miss,n.meas,nrow(d))
+      names(ref)<-c("Variable",paste0("N=",n.meas),"Difference (95 % CI)","Mutually adjusted difference (95 % CI)")
-    names(ls)<-c("Printable table","Deleted due to missingness in adjusted analysis","Number of outcome observations","Length of dataframe")
+    }
+
+    ls<-list(tbl=ref,miss,n.meas,nrow(d),mean_est)
+    names(ls)<-c("Printable table","Deleted due to missingness in adjusted analysis","Number of outcome observations","Length of dataframe","Estimated true mean (95 % CI) in adjusted analysis")
 
   }
 

man/strobe_diff_bygroup.Rd

@@ -11,13 +11,13 @@ strobe_diff_bygroup(meas, var, group, adj, data, dec = 2)
 
 \item{var}{binary exposure variable to compare against (active vs placebo). As string.}
 
+\item{group}{group to compare, as string.}
+
 \item{adj}{variables to adjust for, as string.}
 
 \item{data}{dataframe of data.}
 
 \item{dec}{decimals for results, standard is set to 2. Mean and sd is dec-1. pval has 3 decimals.}
-
-\item{groups}{groups to compare, as string.}
 }
 \description{
 Printable table of three dimensional regression analysis of group vs var for meas. By group.

man/strobe_diff_byvar.Rd

@@ -11,13 +11,13 @@ strobe_diff_byvar(meas, var, group, adj, data, dec = 2)
 
 \item{var}{binary exposure variable to compare against (active vs placebo). As string.}
 
+\item{group}{groups to compare, as string.}
+
 \item{adj}{variables to adjust for, as string.}
 
 \item{data}{dataframe of data.}
 
 \item{dec}{decimals for results, standard is set to 2. Mean and sd is dec-1.}
-
-\item{groups}{groups to compare, as string.}
 }
 \description{
 Printable table of three dimensional regression analysis of group vs var for meas. By var. Includes p-values.

man/strobe_olr.Rd

@@ -2,7 +2,7 @@
 % Please edit documentation in R/strobe_olr.R
 \name{strobe_olr}
 \alias{strobe_olr}
-\title{Print regression results according to STROBE}
+\title{Print ordinal logistic regression results according to STROBE}
 \usage{
 strobe_olr(meas, vars, data, dec = 2)
 }
@@ -16,6 +16,6 @@ strobe_olr(meas, vars, data, dec = 2)
 \item{dec}{decimals for results, standard is set to 2. Mean and sd is dec-1.}
 }
 \description{
-Printable table of logistic regression analysis oaccording to STROBE.
+Printable table of ordinal logistic regression analysis oaccording to STROBE. Uses polr() funtion of the MASS-package.
 }
 \keyword{olr}

man/strobe_pred.Rd

@@ -4,7 +4,8 @@
 \alias{strobe_pred}
 \title{Regression model of predictors according to STROBE, bi- and multivariate.}
 \usage{
-strobe_pred(meas, adj, data, dec = 2, n.by.adj = FALSE)
+strobe_pred(meas, adj, data, dec = 2, n.by.adj = FALSE,
+  p.val = FALSE)
 }
 \arguments{
 \item{meas}{binary outcome meassure variable, column name in data.frame as a string. Can be numeric or factor. Result is calculated accordingly.}
@@ -16,8 +17,10 @@ strobe_pred(meas, adj, data, dec = 2, n.by.adj = FALSE)
 \item{dec}{decimals for results, standard is set to 2. Mean and sd is dec-1.}
 
 \item{n.by.adj}{flag to indicate wether to count number of patients in adjusted model or overall for outcome meassure not NA.}
+
+\item{p.val}{flag to include p-values in linear regression for now, set to FALSE as standard.}
 }
 \description{
-Printable table of regression model according to STROBE. Includes borth bivariate and multivariate in the same table. Output is a list, with the first item being the main "output" as a dataframe. Automatically uses logistic regression model for dichotomous outcome variable and linear regression model for continous outcome variable.
+Printable table of regression model according to STROBE. Includes borth bivariate and multivariate in the same table. Output is a list, with the first item being the main "output" as a dataframe. Automatically uses logistic regression model for dichotomous outcome variable and linear regression model for continous outcome variable. Linear regression will give estimated adjusted true mean in list.
 }
 \keyword{logistic}