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lifeTables.R
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lifeTables.R
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lifeTables <- function(survivalTime, statusVariable, status, factors, fromTime = 0, toTime = 60, by = 12,
lifeTable = TRUE, descriptives = TRUE, hr=TRUE, medianLifeTime = FALSE, ci = "log",
varianceEstimation = "greenwood", compare = TRUE, comparisonTest = "logRank", confidenceLevel = 95,
referenceCategory = "first", typeOfTest = "asymptotic", q = 1, p = 1, data = dataSet){
if(!is.null(survivalTime)){
survivalTime = as.matrix(data[, survivalTime, drop = FALSE])
survivalTime = apply(survivalTime, 2, as.numeric)
}
if(!is.null(factors)){
factors = as.factor(data[, factors])
}
if(referenceCategory != "first"){
factors <- factor(factors, levels=rev(levels(factors)))
}
if(!is.null(statusVariable)){
statusVariable = data[, statusVariable]
}
if(!is.null(status)){
if(is.numeric(status)){status = as.numeric(status)}else{status = as.factor(status)}
}
if(!is.null(factors)){
newData = cbind.data.frame(id =seq(1,dim(survivalTime)[1], 1), survivalTime= survivalTime[,1],
statusVar=statusVariable,factor = factors)
newData = newData[complete.cases(newData),]
}else{
newData = data.frame(id =seq(1,dim(survivalTime)[1], 1), survivalTime= survivalTime[,1],
statusVar=statusVariable)
newData = newData[complete.cases(newData),]
}
newData$statusVar = newData$statusVar%in%status
if(is.null(factors)){
if(lifeTable){
tis <- seq(fromTime, toTime, by)
nevent = 0
nlost = 0
nsubs = dim(newData)[1]
lifeTableFormat = list()
for(i in 1:(length(tis)-1)){
subData = newData[newData$survivalTime>=tis[i] & newData$survivalTime<tis[i+1],]
time = paste(tis[i], tis[i+1], sep = "_<")
nsubs = nsubs-nevent-nlost
nevent = sum(subData$statusVar)
nlost = nrow(subData)-nevent
lifeTableFormat [[i]] = data.frame(time, nsubs, nlost, nevent)
}
lifeTableResult <- do.call(rbind.data.frame, lifeTableFormat)
lifeTableResult$nrisk = as.numeric(formatC(lifeTableResult$nsubs - (lifeTableResult$nlost/2), digits = 3, format = "f"))
lifeTableResult$qt = formatC(lifeTableResult$nevent/lifeTableResult$nrisk, digits = 3, format = "f")
lifeTableResult$pt = formatC(1-as.numeric(lifeTableResult$qt), digits = 3, format = "f")
lifeTableResult$Pt[1] = 1
for(j in 2:nrow(lifeTableResult)){
lifeTableResult$Pt[j] = formatC(as.numeric(lifeTableResult$pt[j-1])*as.numeric(lifeTableResult$Pt[j-1]), digits = 3, format = "f")
}
lifeTableRes = lifeTableResult[c(1:3,5,4,6:8)]
colnames(lifeTableRes) = c("interval time", "n of subjects", "n of withdraws", "n of subjects at risk", "n of events", "prob. of terminating during the time interval", "prob. of surviving in the time interval", "cum. prob. of surviving at the begining of the time interval")
}else{lifeTableRes = NULL}
if(hr){
surv = as.data.frame(KMsurv::lifetab(tis, lifeTableResult$nsubs[1], lifeTableResult$nlost, lifeTableResult$nevent))
hazardRatio = surv[c(7,10)]
hazardRatio$lowerLimit = as.numeric(formatC(hazardRatio$hazard - qnorm(1-(1-confidenceLevel/100)/2)*hazardRatio$se.hazard, digits = 3, format = "f"))
hazardRatio$upperLimit = as.numeric(formatC(hazardRatio$hazard + qnorm(1-(1-confidenceLevel/100)/2)*hazardRatio$se.hazard, digits = 3, format = "f"))
hazardRatio$hazard = as.numeric(formatC(hazardRatio$hazard, digits = 3, format = "f"))
hazardRatio$se.hazard = as.numeric(formatC(hazardRatio$se.hazard, digits = 3, format = "f"))
row.names(hazardRatio) = NULL
hazardRatio = data.frame(time = lifeTableResult$time, hazardRatio)
colnames(hazardRatio) = c("Interval time", "HR", "S.E.", "Lower limit", "Upper limit")
}else{hazardRatio=NULL}
if(medianLifeTime){
surv$time = rownames(surv)
rownames(surv) = NULL
subSurv = surv[surv$surv > 0.5,]
subSurv2 = subSurv[dim(subSurv)[1],]
rowNumber = as.numeric(rownames(subSurv2))
subSurv3 = surv[rowNumber:(rowNumber+1),]
tj = as.numeric(strsplit(subSurv3$time[1], "-")[[1]][1])
tj1 = as.numeric(strsplit(subSurv3$time[1], "-")[[1]][2])
stj = subSurv3$surv[1]
stj1 = subSurv3$surv[2]
wj = tj1-tj
medianLifeTimeResult = as.data.frame(formatC(tj+((stj - 0.5)*wj)/(stj-stj1), digits = 3 , format = "f"))
colnames(medianLifeTimeResult) = "Median life time"
}else{
medianLifeTimeResult = NULL
}
}else{
splitDF = split(newData, newData$factor)
lifeTableResult <- lapply(splitDF, FUN = function(x){
tis <- seq(fromTime, toTime, by)
nevent = 0
nlost = 0
nsubs = dim(x)[1]
lifeTableFormat = list()
for(i in 1:(length(tis)-1)){
subData = x[x$survivalTime>=tis[i] & x$survivalTime<tis[i+1],]
time = paste(tis[i], tis[i+1], sep = "_<")
nsubs = nsubs-nevent-nlost
nevent = sum(subData$statusVar)
nlost = nrow(subData)-nevent
lifeTableFormat[[i]] = data.frame(time, nsubs, nlost, nevent)
}
lifeTableResult <- do.call(rbind.data.frame, lifeTableFormat)
lifeTableResult$nrisk = as.numeric(formatC(lifeTableResult$nsubs - (lifeTableResult$nlost/2), digits = 3, format = "f"))
lifeTableResult$qt = formatC(lifeTableResult$nevent/lifeTableResult$nrisk, digits = 3, format = "f")
lifeTableResult$pt = formatC(1-as.numeric(lifeTableResult$qt), digits = 3, format = "f")
lifeTableResult$Pt[1] = 1
for(j in 2:nrow(lifeTableResult)){
lifeTableResult$Pt[j] = formatC(as.numeric(lifeTableResult$pt[j-1])*as.numeric(lifeTableResult$Pt[j-1]), digits = 3, format = "f")
}
return(lifeTableResult)
})
if(hr){
hazardRatio <- lapply(lifeTableResult, FUN = function(y){
tis <- seq(fromTime, toTime, by)
surv = as.data.frame(KMsurv::lifetab(tis, y$nsubs[1], y$nlost, y$nevent))
hazardRatio = surv[c(7,10)]
hazardRatio$lowerLimit = as.numeric(formatC(hazardRatio$hazard - qnorm(1-(1-confidenceLevel/100)/2)*hazardRatio$se.hazard, digits = 3, format = "f"))
hazardRatio$upperLimit = as.numeric(formatC(hazardRatio$hazard + qnorm(1-(1-confidenceLevel/100)/2)*hazardRatio$se.hazard, digits = 3, format = "f"))
hazardRatio$hazard = as.numeric(formatC(hazardRatio$hazard, digits = 3, format = "f"))
hazardRatio$se.hazard = as.numeric(formatC(hazardRatio$se.hazard, digits = 3, format = "f"))
row.names(hazardRatio) = NULL
hazardRatio = data.frame(time = y$time, hazardRatio)
colnames(hazardRatio) = c("Interval time", "HR", "S.E.", "Lower limit", "Upper limit")
return(hazardRatio)
})
}else{hazardRatio = NULL}
if(medianLifeTime){
medianLifeTimeResult <- lapply(lifeTableResult, FUN = function(y){
tis <- seq(fromTime, toTime, by)
surv = as.data.frame(KMsurv::lifetab(tis, y$nsubs[1], y$nlost, y$nevent))
surv$time = rownames(surv)
rownames(surv) = NULL
subSurv = surv[surv$surv > 0.5,]
subSurv2 = subSurv[dim(subSurv)[1],]
rowNumber = as.numeric(rownames(subSurv2))
subSurv3 = surv[rowNumber:(rowNumber+1),]
tj = as.numeric(strsplit(subSurv3$time[1], "-")[[1]][1])
tj1 = as.numeric(strsplit(subSurv3$time[1], "-")[[1]][2])
stj = subSurv3$surv[1]
stj1 = subSurv3$surv[2]
wj = tj1-tj
medianLifeTimeResult = as.data.frame(formatC(tj+((stj - 0.5)*wj)/(stj-stj1), digits = 3 , format = "f"))
colnames(medianLifeTimeResult) = "Median life time"
return(medianLifeTimeResult)
})
}else{medianLifeTimeResult = NULL}
if(lifeTable){
lifeTableRes <- lapply(lifeTableResult, FUN = function(z){
lifeTable = z[c(1:3,5,4,6:8)]
colnames(lifeTable) = c("interval time", "n of subjects", "n of withdraws", "n of subjects at risk", "n of events", "prob. of terminating during the time interval", "prob. of surviving in the time interval", "cum. prob. of surviving at the begining of the time interval")
return(lifeTable)
})
}else{lifeTableRes = NULL}
}
if(descriptives){
if(is.null(factors)){
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")
}else{
splitFactor = split(newData, newData$factor)
caseSummary <- lapply(splitFactor, FUN = function(x){
n = nrow(x)
nOfEvent = as.numeric(table(x$statusVar)[as.factor(names(table(x$statusVar))) %in% TRUE][[1]])
percentOfEvent = as.numeric(formatC((nOfEvent/n)*100, digits = 3, format = "f"))
nOfCensor = as.numeric(table(x$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{caseSummary = NULL}
if(!is.null(factors)){
assign("newData", newData, envir=.GlobalEnv) # put the dat in the global env
compareCurves <- survfit(Surv(survivalTime, statusVar == TRUE) ~ factor, data = newData, conf.type = ci, error = varianceEstimation, conf.int = confidenceLevel/100)
# remove(newData,envir=.GlobalEnv) # delete dat again from global env
comps = ten(compareCurves)
comp(comps, p = p, q = q)
comparisonTests = as.data.frame(attr(comps, "lrt"))
if(compare){
#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(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(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(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(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(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"))))
#}
testResults = rbind.data.frame(lr, gb, tw, pp, mpp, fh)
colnames(testResults) = c("Test", "Chi square", "df", "p value")
}else{
testResults = NULL
}}else{testResults = NULL}
result = list(tableResult = list(caseSummary = caseSummary, medianLifeTime = medianLifeTimeResult), testResult = list(testResults = testResults, lifeTable = lifeTableRes, hazardRatio = hazardRatio))
return(result)
}