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113 lines
3.3 KiB
R
113 lines
3.3 KiB
R
#' Print regression results according to STROBE
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#'
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#' Printable table of three dimensional regression analysis of group vs var for meas. By var.
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#' @param meas outcome meassure variable name in data-data.frame as a string. Can be numeric or factor. Result is calculated accordingly.
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#' @param var binary exposure variable to compare against (active vs placebo). As string.
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#' @param groups groups to compare, as string.
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#' @param adj variables to adjust for, as string.
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#' @param data dataframe of data.
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#' @param dec decimals for results, standard is set to 2. Mean and sd is dec-1.
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#' @keywords strobe
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#' @export
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#' @examples
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#' strobe_diff_byvar()
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strobe_diff_byvar<-function(meas,var,group,adj,data,dec=2){
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## Wishlist:
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## -fix confint()
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## meas: sdmt
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## var: rtreat
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## group: genotype
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## for dichotome exposure variable (var)
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d<-data
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m<-d[,c(meas)]
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v<-d[,c(var)]
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g<-d[,c(group)]
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ads<-d[,c(adj)]
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dat<-data.frame(m,v,g,ads)
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df<-data.frame(grp=c(NA,as.character(levels(g))))
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if(!is.factor(m)){
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for (i in 1:length(levels(v))){
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grp<-levels(dat$v)[i]
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di<-dat[dat$v==grp,][,-2]
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mod<-lm(m~g,data=di)
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co<-c("-",round(coef(mod)[-1],dec))
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lo<-c("-",round(confint(mod)[-1,1],dec))
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up<-c("-",round(confint(mod)[-1,2],dec))
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ci<-paste0(co," (",lo," to ",up,")")
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amod<-lm(m~.,data=di)
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aco<-c("-",round(coef(amod)[2:length(levels(g))],dec))
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alo<-c("-",round(confint(amod)[2:length(levels(g)),1],dec))
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aup<-c("-",round(confint(amod)[2:length(levels(g)),2],dec))
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aci<-paste0(aco," (",alo," to ",aup,")")
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nr<-c()
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for (r in 1:length(levels(g))){
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vr<-levels(di$g)[r]
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dr<-di[di$g==vr,]
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n<-as.numeric(nrow(dr[!is.na(dr$m),]))
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mean<-round(mean(dr$m,na.rm = TRUE),dec-1)
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sd<-round(sd(dr$m,na.rm = TRUE),dec-1)
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ms<-paste0(mean," (",sd,")")
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nr<-c(nr,n,ms)
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}
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irl<-rbind(matrix(grp,ncol=4),cbind(matrix(nr,ncol=2,byrow = TRUE),cbind(ci,aci)))
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colnames(irl)<-c("N","Mean (SD)","Difference","Adjusted Difference")
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df<-cbind(df,irl)
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}}
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if(is.factor(m)){
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for (i in 1:length(levels(v))){
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grp<-levels(dat$v)[i]
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di<-dat[dat$v==grp,][,-2]
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mod<-glm(m~g,family=binomial(),data=di)
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co<-c("-",round(exp(coef(mod)[-1]),dec))
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lo<-c("-",round(exp(confint(mod)[-1,1]),dec))
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up<-c("-",round(exp(confint(mod)[-1,2]),dec))
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ci<-paste0(co," (",lo," to ",up,")")
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amod<-glm(m~.,family=binomial(),data=di)
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aco<-c("-",suppressMessages(round(exp(coef(amod)[2:length(levels(g))]),dec)))
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alo<-c("-",suppressMessages(round(exp(confint(amod)[2:length(levels(g)),1]),dec)))
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aup<-c("-",suppressMessages(round(exp(confint(amod)[2:length(levels(g)),2]),dec)))
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aci<-paste0(aco," (",alo," to ",aup,")")
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nr<-c()
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for (r in 1:length(levels(g))){
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vr<-levels(di$g)[r]
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dr<-di[di$g==vr,]
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n<-as.numeric(nrow(dr[!is.na(dr$m),]))
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nl<-levels(m)[2]
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out<-nrow(dr[dr$m==nl&!is.na(dr$m),])
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pro<-round(out/n*100,0)
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rt<-paste0(out," (",pro,"%)")
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nr<-c(nr,n,rt)
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}
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irl<-rbind(matrix(grp,ncol=4),cbind(matrix(nr,ncol=2,byrow = TRUE),cbind(ci,aci)))
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colnames(irl)<-c("N",paste0("N.",nl),"OR","Adjusted OR")
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df<-cbind(df,irl)
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}}
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return(df)
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}
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