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kaplanMeier.R
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kaplanMeier.R
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kaplanMeier <- function(survivalTime, statusVariable, status, factors, survivalTable = TRUE, caseSummary = TRUE, hr=TRUE,
meanMedianSurvivalTimes = TRUE, quartilesOfSurvivalTimes = FALSE, ci = "log",
varianceEstimation = "greenwood", comparisonTest = "logRank", confidenceLevel = 95,
referenceCategory = "first", typeOfTest = "asymptotic", kmCurve = TRUE, p= 1, q = 1, data = dataSet){
fname = factors
if(!is.null(survivalTime)){
survivalTime = as.matrix(data[, survivalTime, drop = FALSE])
survivalTime = apply(survivalTime, 2, as.numeric)
if(sum(survivalTime[,1], na.rm = T) == 0){
stop("Survival time is not a numeric variable.")
}
}
if(!is.null(factors)){
factorsName = data[, factors, drop = FALSE]
}
if(!is.null(factors)){
factors = as.factor(data[, factors])
}
if(referenceCategory != "first" && !is.null(factors)){
factors <- factor(factors, levels=rev(levels(factors)))
}
if(!is.null(statusVariable)){
statusVariable = data[, statusVariable]
}
if(!is.null(status)){
if(is.numeric(status)){status = as.factor(status)}else{status = as.factor(status)}
}
if(!is.null(factors)){
newData = data.frame(id =seq(1,dim(survivalTime)[1], 1), survivalTime= survivalTime,
statusVar=statusVariable,factor = factors)
newData = newData[complete.cases(newData),]
#newData$survivalTime = as.numeric(newData$survivalTime)
colnames(newData) = c("id", "time", "statusVar", "factor")
}else{
newData = data.frame(id =seq(1,dim(survivalTime)[1], 1), survivalTime= survivalTime,
statusVar=statusVariable)
newData = newData[complete.cases(newData),]
# newData$survivalTime = as.numeric(newData$survivalTime)
colnames(newData) = c("id", "time", "statusVar")
}
newData$statusVar = newData$statusVar%in%status
if(!is.null(factors)){
if(caseSummary){
splitFactor = split(newData, newData$factor)
caseSummary <- lapply(splitFactor, FUN = function(x){
if(TRUE %in% x$statusVar){
n = nrow(x)
nOfEvent = as.numeric(table(x$statusVar)[as.factor(names(table(x$statusVar))) %in% TRUE][[1]])
percentOfEvent = as.numeric(formatC((nOfEvent/n)*100, digits = 3, format = "f"))
if(percentOfEvent == 100){
nOfCensor = as.numeric(formatC(0, digits = 3, format = "f"))
percentOfCensor = as.numeric(formatC(0, digits = 3, format = "f"))
}else{
nOfCensor = as.numeric(table(x$statusVar)[(!(names(table(x$statusVar))) %in% TRUE)][[1]])
percentOfCensor = as.numeric(formatC((nOfCensor/n)*100, digits = 3, format = "f"))
}
caseSummary = data.frame(n,nOfEvent, percentOfEvent, nOfCensor, percentOfCensor)
colnames(caseSummary) = c("n", "n of event", "% of event", "n of censor", "% of censor")
return(caseSummary)
}else{
stop("Factor variable is not appropriate. Not enough events in at least one of the factor groups.")
}
})
}
assign("newData", newData, envir=.GlobalEnv) # put the dat in the global env
compareCurves <- survfit(Surv(time, statusVar) ~ factor, data = newData, conf.type = ci, error = varianceEstimation, conf.int = confidenceLevel/100)
summary = summary(compareCurves, rmean = "individual")
if(survivalTable){
survivalTableResult = data.frame(summary[c(2:4,8,6,9,11,10)])
survivalTableResult$surv = as.numeric(formatC(summary$surv, digits = 3, format = "f"))
survivalTableResult$std.err = as.numeric(formatC(summary$std.err, digits = 3, format = "f"))
survivalTableResult$upper = as.numeric(formatC(summary$upper, digits = 3, format = "f"))
survivalTableResult$lower = as.numeric(formatC(summary$lower, digits = 3, format = "f"))
survivalTableResult = split(survivalTableResult[-4], survivalTableResult$strata)
names(survivalTableResult) = levels(factors)
survivalTableLastResult = lapply(survivalTableResult, function(x)
{
colnames(x) = c("Time", "Number at risk", "Number of event", "Cumulative probability of surviving", "S.E.", "Lower limit", "Upper limit")
return(x)
}
)
}else{
survivalTableLastResult = NULL
}
if(hr){
if(!is.null(survivalTableResult)){
hazardRatio = lapply(survivalTableResult, function(x)
{
survHat = x$surv
lowerLimit = x$upper
upperLimit = x$lower
hazard = data.frame(Time = x$time, Hazard = as.numeric(formatC(-log(survHat), digits = 3, format = "f")), Lower = as.numeric(formatC(-log(lowerLimit), digits = 3, format = "f")), Upper = as.numeric(formatC(-log(upperLimit), digits = 3, format = "f")))
return(hazard)
})
}else{hazardRatio = NULL}
}else{hazardRatio = NULL}
if(meanMedianSurvivalTimes){
meanMedian = as.data.frame(summary$table[,-c(2:4)])
meanMedian$meanLL = meanMedian$`*rmean` - qnorm(1-((1-confidenceLevel/100)/2))*meanMedian$`*se(rmean)`
meanMedian$meanUL = meanMedian$`*rmean` + qnorm(1-((1-confidenceLevel/100)/2))*meanMedian$`*se(rmean)`
meanMedian$factor = levels(factors)
row.names(meanMedian) = NULL
meanMedian2 = meanMedian[c(9,1,2:3,7:8,4:6)]
colnames(meanMedian2) = c("Factor", "n", "Mean", "S.E.(mean)", "Lower (mean)", "Upper (mean)", "Median", "Lower (median)", "Upper (median)")
meanMedian2$Mean = as.numeric(formatC(meanMedian2$Mean, digits = 3, format ="f"))
meanMedian2$`S.E.(mean)` = as.numeric(formatC(meanMedian2$`S.E.(mean)`, digits = 3, format ="f"))
meanMedian2$`Lower (mean)` = as.numeric(formatC(meanMedian2$`Lower (mean)`, digits = 3, format ="f"))
meanMedian2$`Upper (mean)` = as.numeric(formatC(meanMedian2$`Upper (mean)`, digits = 3, format ="f"))
meanMedian2$Median = as.numeric(formatC(meanMedian2$Median, digits = 3, format ="f"))
meanMedian2$`Lower (median)` = as.numeric(formatC(meanMedian2$`Lower (median)`, digits = 3, format ="f"))
meanMedian2$`Upper (median)` = as.numeric(formatC(meanMedian2$`Upper (median)`, digits = 3, format ="f"))
}else{meanMedian2 = NULL}
if(quartilesOfSurvivalTimes){
quan = as.data.frame(quantile(compareCurves)$quantile)
names(quan) = c("25%", "50% (median)", "75%")
quan2 = cbind(Factor = levels(factors), quan)
row.names(quan2) = NULL
}else{quan2 = NULL}
comps = ten(compareCurves)
tryCatch(
{
comp(comps, p = p, q = q)
},
error=function(cond) {
stop("Cannot take the inverse of the design matrix. Please check your Survival time, Status and Factor variables.")
}
)
comparisonTests = as.data.frame(attr(comps, "lrt"))
#$tests$lrTests
#if(comparisonTest == "logRank"){
lr = data.frame(cbind(Test = "Log-rank", Chi_square= as.numeric(formatC(comparisonTests[1,6], digits = 3, format = "f")), DF = comparisonTests[1,7], p_value= as.numeric(formatC(comparisonTests[1,8], digits = 3, format = "f"))))
#}
if(is.na(lr$Chi_square)){
stop("Variables are inappropriate, please check your Survival time, Status and Factor variables.")
}
#if(comparisonTest == "gehanBreslow"){
gb = data.frame(cbind(Test = "Gehan-Breslow", Chi_square= as.numeric(formatC(comparisonTests[2,6], digits = 3, format = "f")), DF = comparisonTests[2,7], p_value= as.numeric(formatC(comparisonTests[2,8], digits = 3, format = "f"))))
#}
if(is.na(gb$Chi_square)){
stop("Variables are inappropriate, please check your Survival time, Status and Factor variables.")
}
#if(comparisonTest == "taroneWare"){
tw = data.frame(cbind(Test = "Tarone-Ware", Chi_square= as.numeric(formatC(comparisonTests[3,6], digits = 3, format = "f")), DF = comparisonTests[3,7], p_value= as.numeric(formatC(comparisonTests[3,8], digits = 3, format = "f"))))
#}
if(is.na(tw$Chi_square)){
stop("Variables are inappropriate, please check your Survival time, Status and Factor variables.")
}
#if(comparisonTest == "petoPeto"){
pp = data.frame(cbind(Test = "Peto-Peto", Chi_square= as.numeric(formatC(comparisonTests[4,6], digits = 3, format = "f")), DF = comparisonTests[4,7], p_value= as.numeric(formatC(comparisonTests[4,8], digits = 3, format = "f"))))
#}
if(is.na(pp$Chi_square)){
stop("Variables are inappropriate, please check your Survival time, Status and Factor variables.")
}
#if(comparisonTest == "modPetoPeto"){
mpp = data.frame(cbind(Test = "Modified Peto-Peto", Chi_square= as.numeric(formatC(comparisonTests[5,6], digits = 3, format = "f")), DF = comparisonTests[5,7], p_value= as.numeric(formatC(comparisonTests[5,8], digits = 3, format = "f"))))
#}
if(is.na(mpp$Chi_square)){
stop("Variables are inappropriate, please check your Survival time, Status and Factor variables.")
}
#if(comparisonTest == "flemingtonHarnington"){
fh = data.frame(cbind(Test = paste0("Flemington-Harnington p = ",p, " q = ", q), Chi_square= as.numeric(formatC(comparisonTests[6,6], digits = 3, format = "f")), DF = comparisonTests[6,7], p_value= as.numeric(formatC(comparisonTests[6,8], digits = 3, format = "f"))))
#}
if(is.na(fh$Chi_square)){
stop("Variables are inappropriate, please check your Survival time, Status and Factor variables.")
}
testResults = rbind.data.frame(lr, gb, tw, pp, mpp, fh)
colnames(testResults) = c("Test", "Chi square", "df", "p value")
}else{
if(caseSummary){
n = nrow(newData)
nOfEvent = as.numeric(table(newData$statusVar)[as.factor(names(table(newData$statusVar))) %in% TRUE][[1]])
percentOfEvent = as.numeric(formatC((nOfEvent/n)*100, digits = 3, format = "f"))
nOfCensor = as.numeric(table(newData$statusVar)[(!(names(table(newData$statusVar))) %in% TRUE)][[1]])
percentOfCensor = as.numeric(formatC((nOfCensor/n)*100, digits = 3, format = "f"))
caseSummary = data.frame(n,nOfEvent, percentOfEvent, nOfCensor, percentOfCensor)
colnames(caseSummary) = c("n", "n of event", "% of event", "n of censor", "% of censor")
}
assign("newData", newData, envir=.GlobalEnv) # put the dat in the global env
compareCurves <- survfit(Surv(time, statusVar == TRUE) ~ 1, data = newData, conf.type = ci, error = varianceEstimation, conf.int = confidenceLevel/100)
summary = summary(compareCurves, rmean = "individual")
if(survivalTable){
survivalTableResult = data.frame(summary[c(2:4,6,8,10,9)])
survivalTableResult$surv = as.numeric(formatC(survivalTableResult$surv, digits = 3, format = "f"))
survivalTableResult$std.err = as.numeric(formatC(survivalTableResult$std.err, digits = 3, format = "f"))
survivalTableResult$upper = as.numeric(formatC(survivalTableResult$upper, digits = 3, format = "f"))
survivalTableResult$lower = as.numeric(formatC(survivalTableResult$lower, digits = 3, format = "f"))
survivalTableLastResult = survivalTableResult[-8]
colnames(survivalTableLastResult) = c("Time", "Number at risk", "Number of event", "Cumulative probability of surviving", "S.E.", "Upper limit", "Lower limit")
}else{
survivalTableLastResult = NULL
}
if(hr){
if(!is.null(survivalTableResult)){
survHat = survivalTableResult$surv
lowerLimit = survivalTableResult$upper
upperLimit = survivalTableResult$lower
hazardRatio = data.frame(Time = survivalTableResult$time, Hazard = as.numeric(formatC(-log(survHat), digits = 3, format = "f")), Lower = as.numeric(formatC(-log(lowerLimit), digits = 3, format = "f")), Upper = as.numeric(formatC(-log(upperLimit), digits = 3, format = "f")))
}else{hazardRatio = NULL}
}else{hazardRatio = NULL}
if(meanMedianSurvivalTimes){
meanMedian = as.data.frame(t(summary$table))
meanMedian$meanLL = meanMedian$`*rmean` - qnorm(1-((1-confidenceLevel/100)/2))*meanMedian$`*se(rmean)`
meanMedian$meanUL = meanMedian$`*rmean` + qnorm(1-((1-confidenceLevel/100)/2))*meanMedian$`*se(rmean)`
row.names(meanMedian) = NULL
meanMedian2 = meanMedian[c(1,5:6,10:11,7:9)]
colnames(meanMedian2) = c("n", "Mean", "S.E.(mean)", "Lower (mean)", "Upper (mean)", "Median", "Lower (median)", "Upper (median)")
meanMedian2$Mean = as.numeric(formatC(meanMedian2$Mean, digits = 3, format ="f"))
meanMedian2$`S.E.(mean)` = as.numeric(formatC(meanMedian2$`S.E.(mean)`, digits = 3, format ="f"))
meanMedian2$`Lower (mean)` = as.numeric(formatC(meanMedian2$`Lower (mean)`, digits = 3, format ="f"))
meanMedian2$`Upper (mean)` = as.numeric(formatC(meanMedian2$`Upper (mean)`, digits = 3, format ="f"))
meanMedian2$Median = as.numeric(formatC(meanMedian2$Median, digits = 3, format ="f"))
meanMedian2$`Lower (median)` = as.numeric(formatC(meanMedian2$`Lower (median)`, digits = 3, format ="f"))
meanMedian2$`Upper (median)` = as.numeric(formatC(meanMedian2$`Upper (median)`, digits = 3, format ="f"))
}else{meanMedian2 = NULL}
if(quartilesOfSurvivalTimes){
quan2 = as.data.frame(t(quantile(compareCurves)$quantile))
names(quan2) = c("25%", "50% (median)", "75%")
row.names(quan2) = NULL
}else{quan2 = NULL}
testResults = NULL
}
result = list(tableResult = list(caseSummary = caseSummary, meanMedianSurvivalTimes = meanMedian2, quartilesOfSurvivalTimes = quan2), testResult = list(testResults = testResults, survivalTable = survivalTableLastResult, hazardRatio = hazardRatio))
return(result)
}