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survivalCutOff.R
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survivalCutOff.R
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data = read.table("~/Dropbox/GSD/Studies/Web-Tools(Devel)/compSurv/www/data/brainCancer.txt", header = T)
markers = "age"
survivalTime = "time"
statusVariable = "status"
status = 1
# cutoff <- cutoff.survival(marker, time, event, nmin = 1)
#
# kaplanMeier(survivalTime = "time", statusVariable = "status", status = 1, factors = NULL, data = data)
cuttOffForSurvival(markers, survivalTime, statusVariable, status, compTest = "flemingtonHarrington", p= 0, q = 0, nmin=1,
confidenceLevel=95, data = data)
cuttOffForSurvival <- function(markers, survivalTime, statusVariable, status, compTest = "logRank", method = "significance", p= 1, q = 1, nmin=1,
confidenceLevel=95, data = data){
# cat(paste("Optimizing cutoff using method survival", method, sep="_"))
markerData = data[,markers, drop = F]
survivalTimeData = data[,survivalTime, drop = F]
statusVariableData = data[, statusVariable, drop = F]
survivalTimeName <- names(survivalTimeData)
endtime = max(survivalTimeData[,1])
optimalCutoffs <- matrix(nrow= length(markers), ncol=10)
colnames(optimalCutoffs) <- c("Optimal cut-off", "HR", "HR (lower)", "HR (upper)", "Mean survival time (lower)", "SD survival time (lower)", "Mean survival time (upper)", "SD survival time (upper)", "testStatistic", "p value")
for(m in 1:ncol(markerData)){
markerName <- names(markerData[m])
newData = cbind.data.frame(markerData[,m], survivalTimeData, statusVariableData)
colnames(newData)[1] = markerName
newData = newData[complete.cases(newData),]
npatients <- nrow(newData)
marker = newData[1]
time = newData[2]
event = newData[3]
index <- intersect(which(!is.na(marker)), intersect(which(!is.na(time)), which(!is.na(event))))
marker <- marker[index,]
time <- time[index,]
event <- event[index,]
n <- length(marker)
nevent <- length(which(event == status))
nmarker <- length(unique(marker))
if (nmarker < 3) stop("insufficient data")
index <- order(marker)
marker <- marker[index]
time <- time[index]
event <- event[index]
event = event == status
y <- Surv(time, event)
q <- 1-(1-confidenceLevel/100)/2
z <- qnorm(q)
nlow <- nmin:(n-nmin)
Y <- matrix(nrow=length(nlow), ncol=10)
rownames(Y) <- nlow
for (i in nlow) {
j <- i-nmin+1
if (marker[i] != marker[i+1]) {
x <- c(rep(0, i), rep(1, n-i))
model <- summary(coxph(Surv(time, event) ~ x))
coef <- model$coefficients
Y[j, 1] <- (marker[i] + marker[i+1]) / 2
Y[j, 2] <- coef[2]
Y[j, 3] <- exp(coef[1] - z * coef[3])
Y[j, 4] <- exp(coef[1] + z * coef[3])
if(compTest == "logRank"){
newData2 = cbind.data.frame("x2" = x,"time2" = time, "event2" = event)
test <- coxph(Surv(time2, event2) ~ x2, data = newData2)
comps <- ten(test)
comp(comps)
comparisonTests = as.data.frame(attr(comps, "lrt"))
Y[j, 9] <- comparisonTests[1,6]
Y[j, 10] <- comparisonTests[1,8]
fit <- summary(survfit(y ~ x), rmean=endtime)
tab <- fit$table
index.low <- grep(0, rownames(tab))
Y[j, 5] <- tab[index.low, "*rmean"]
Y[j, 6] <- tab[index.low, "*se(rmean)"]
index.high <- grep(1, rownames(tab))
Y[j, 7] <- tab[index.high, "*rmean"]
Y[j, 8] <- tab[index.high, "*se(rmean)"]
}
if(compTest == "gehanBreslow"){
newData2 = cbind.data.frame("x2" = x,"time2" = time, "event2" = event)
test <- coxph(Surv(time2, event2) ~ x2, data = newData2)
comps <- ten(test)
comp(comps)
comparisonTests = as.data.frame(attr(comps, "lrt"))
Y[j, 9] <- comparisonTests[2,6]
Y[j, 10] <- comparisonTests[2,8]
fit <- summary(survfit(y ~ x), rmean=endtime)
tab <- fit$table
index.low <- grep(0, rownames(tab))
Y[j, 5] <- tab[index.low, "*rmean"]
Y[j, 6] <- tab[index.low, "*se(rmean)"]
index.high <- grep(1, rownames(tab))
Y[j, 7] <- tab[index.high, "*rmean"]
Y[j, 8] <- tab[index.high, "*se(rmean)"]
}
if(compTest == "taroneWare"){
newData2 = cbind.data.frame("x2" = x,"time2" = time, "event2" = event)
test <- coxph(Surv(time2, event2) ~ x2, data = newData2)
comps <- ten(test)
comp(comps)
comparisonTests = as.data.frame(attr(comps, "lrt"))
Y[j, 9] <- comparisonTests[3,6]
Y[j, 10] <- comparisonTests[3,8]
fit <- summary(survfit(y ~ x), rmean=endtime)
tab <- fit$table
index.low <- grep(0, rownames(tab))
Y[j, 5] <- tab[index.low, "*rmean"]
Y[j, 6] <- tab[index.low, "*se(rmean)"]
index.high <- grep(1, rownames(tab))
Y[j, 7] <- tab[index.high, "*rmean"]
Y[j, 8] <- tab[index.high, "*se(rmean)"]
}
if(compTest == "petoPeto"){
newData2 = cbind.data.frame("x2" = x,"time2" = time, "event2" = event)
test <- coxph(Surv(time2, event2) ~ x2, data = newData2)
comps <- ten(test)
comp(comps)
comparisonTests = as.data.frame(attr(comps, "lrt"))
Y[j, 9] <- comparisonTests[4,6]
Y[j, 10] <- comparisonTests[4,8]
fit <- summary(survfit(y ~ x), rmean=endtime)
tab <- fit$table
index.low <- grep(0, rownames(tab))
Y[j, 5] <- tab[index.low, "*rmean"]
Y[j, 6] <- tab[index.low, "*se(rmean)"]
index.high <- grep(1, rownames(tab))
Y[j, 7] <- tab[index.high, "*rmean"]
Y[j, 8] <- tab[index.high, "*se(rmean)"]
}
if(compTest == "modifiedPetoPeto"){
newData2 = cbind.data.frame("x2" = x,"time2" = time, "event2" = event)
test <- coxph(Surv(time2, event2) ~ x2, data = newData2)
comps <- ten(test)
comp(comps)
comparisonTests = as.data.frame(attr(comps, "lrt"))
Y[j, 9] <- comparisonTests[5,6]
Y[j, 10] <- comparisonTests[5,8]
fit <- summary(survfit(y ~ x), rmean=endtime)
tab <- fit$table
index.low <- grep(0, rownames(tab))
Y[j, 5] <- tab[index.low, "*rmean"]
Y[j, 6] <- tab[index.low, "*se(rmean)"]
index.high <- grep(1, rownames(tab))
Y[j, 7] <- tab[index.high, "*rmean"]
Y[j, 8] <- tab[index.high, "*se(rmean)"]
}
if(compTest == "flemingtonHarrington"){
newData2 = cbind.data.frame("x2" = x,"time2" = time, "event2" = event)
test <- coxph(Surv(time2, event2) ~ x2, data = newData2)
comps <- ten(test)
comp(comps, p = p, q = q)
comparisonTests = as.data.frame(attr(comps, "lrt"))
Y[j, 9] <- comparisonTests[1,6]
Y[j, 10] <- comparisonTests[1,8]
fit <- summary(survfit(y ~ x), rmean=endtime)
tab <- fit$table
index.low <- grep(0, rownames(tab))
Y[j, 5] <- tab[index.low, "*rmean"]
Y[j, 6] <- tab[index.low, "*se(rmean)"]
index.high <- grep(1, rownames(tab))
Y[j, 7] <- tab[index.high, "*rmean"]
Y[j, 8] <- tab[index.high, "*se(rmean)"]
}
}
else Y[j, 10] <- 2
}
if (method == "significance") {
# index.optimal <- which.min(as.numeric(Y[, "p"]))
index.optimal <- which.max(as.numeric(Y[, 9]))
rownames(Y)[index.optimal] <- "optimal"
}
index.p <- which(colnames(Y) == "p")
colnames(Y)[index.p] <- "p_value"
optimalCutoffs[m,] <- formatC(Y[rownames(Y) == "optimal",], digits = 3, format = "f")
}
result = as.data.frame(optimalCutoffs)[-9]
rownames(result) = markers
return(result)
}