#' A complete table for multiple univariable survival analysis
#'
#'JS.uni_m output the table with general multivariable survival analysis result with Number of total patients,
#'Number of Events, HR (95\% Confidence Interval),P value. This function only change the format of the output table.
#'Note: c index and d index are from package survcomp.
#'@param D A data.frame in which to interpret the variables
#'@param Event The status indicator, normally 0=alive, 1=dead
#'@param Stime This is the follow up time
#'@param Svars A vector of variables
#'@param groupns A text vector of the the group names for output
#'@param Cats a vector of logical elements indicating whether or not Svar is a categorical varaible
#'@param EM a logcial term, include estimated median survival nor not
#'@return A dataframe of coxph output including Number of total patients, Number of Events, HRs (95\% Confidence Intervals), P values, C index and D index.
#'@examples
#'Event <- c("pd_censor")
#'Stime <- c("pd_surv")
#'Svars <- c("tr_group", "age_m")
#'Cats <- c(T, F)
#'Groupns <- c("Treatment", "Age")
#'JS.uni_m(D, Event, Stime, Svars, Cats, Groupns)
#'
#'@export
#'@name JS.uni_m
#'
#'
JS.uni_m <- function(Data , Event, Stime , Svars, Cats, Groupns, EM = F) {
rs.all <- NULL
if (EM == F){
for (i in 1:length(Svars))
{
rs <- JS.uni(Data, Event, Stime, Svars[i] , Groupns[i], Cats[i])
rs.all <- rbind(rs.all, rs)
}
}
if (EM == T){
for (i in 1:length(Svars))
{
rs <- JS.uni_em(Data, Event, Stime, Svars[i] , Groupns[i], Cats[i])
rs.all <- rbind(rs.all, rs)
}
}
return(rs.all)
}
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