utils::globalVariables(c("old.d2", "surv", "event", "n.risk", "part"))
DynNom.coxph <- function(model, data, clevel = 0.95, m.summary = c("raw", "formatted"),
covariate = c("slider", "numeric"), ptype = c("st", "1-st")) {
data <- data.frame(data)
tt <- names(attr(model$terms, "dataClasses"))[1]
if (substr(tt,1,5) != "Surv("){
stop("Error in model syntax: 'time' and 'status' do not find in model's terms")
}
if (length(dim(data)) > 2)
stop("Error in data format: dataframe format required")
if (attr(model$terms, "dataClasses")[[1]] == "logical")
stop("Error in model syntax: logical form for response not supported")
if (tail(names(attr(model$terms,"dataClasses")),n=1)=="(weights)") {
n.terms <- length(attr(model$terms,"dataClasses"))
attr(model$terms,"dataClasses") <- attr(model$terms,"dataClasses")[1:n.terms - 1]
}
if (attr(model, "class")[1] == "coxph.null") {
stop("Error in model syntax: the model is null")
}
n.strata <- length(attr(model$terms, "specials")$strata)
dim.terms <- length(names(attr(model$terms, "dataClasses")))
for (i in 2:dim.terms) {
if (substr(names(attr(model$terms, "dataClasses"))[i], 1, 6) == "strata") {
nch <- nchar(names(attr(model$terms, "dataClasses"))[i])
names(attr(model$terms, "dataClasses"))[i] <- substr(names(attr(model$terms,
"dataClasses"))[i], 8, (nch - 1))
}
}
if (!is.null(attr(model$terms, "specials")$tt)) {
stop("Error in model syntax: coxph models with a time dependent covariate is not supported")
}
for(i in 2:length(names(attr(model$terms, "dataClasses")))) {
com1 = numeric(length(names(data)))
for(j in 1:length(names(data))) {
if (names(attr(model$terms, "dataClasses"))[i] == names(data)[j]) com1[j] = 1
}
if (sum(com1) == 0)
stop("Error in model syntax: some of model's terms do not match to variables' name in dataset")
}
coll <- c(1:50)
covariate <- match.arg(covariate)
m.summary <- match.arg(m.summary)
ptype <- match.arg(ptype)
input.data <- NULL
old.d <- NULL
runApp(list(
ui = bootstrapPage(fluidPage(
titlePanel("Dynamic Nomogram"),
sidebarLayout(sidebarPanel(uiOutput("manySliders.f"),
uiOutput("manySliders.n"),
checkboxInput("trans", "Alpha blending (transparency)", value = TRUE),
actionButton("add", "Predict"),
br(), br(),
helpText("Press Quit to exit the application"),
actionButton("quit", "Quit")
),
mainPanel(tabsetPanel(id = "tabs",
tabPanel("Estimated S(t)", plotOutput("plot")),
tabPanel("Predicted Survival", plotlyOutput("plot2")),
tabPanel("Numerical Summary", verbatimTextOutput("data.pred")),
tabPanel("Model Summary", verbatimTextOutput("summary"))
)
)
))),
server = function(input, output){
observe({
if (input$quit == 1)
stopApp()
})
neededVar <- names(attr(model$terms, "dataClasses"))[-1]
if (length(attr(model$terms, "term.labels")) == 1) {
input.data <<- data.frame(data[1, neededVar])
names(input.data)[1] <<- names(attr(model$terms, "dataClasses"))[-1]
} else {
input.data <<- data[1, neededVar]
}
input.data[1, ] <<- NA
b <- 1
i.factor <- NULL
i.numeric <- NULL
for (j in 2:length(attr(model$terms, "dataClasses"))) {
for (i in 1:length(data)) {
if (names(attr(model$terms, "dataClasses"))[j] == names(data)[i]) {
if (attr(model$terms, "dataClasses")[[j]] == "factor" |
attr(model$terms, "dataClasses")[[j]] == "ordered" |
attr(model$terms, "dataClasses")[[j]] == "logical") {
i.factor <- rbind(i.factor, c(names(attr(model$terms, "dataClasses"))[j], j, i, b))
(break)()
}
if (attr(model$terms, "dataClasses")[[j]] == "numeric") {
i.numeric <- rbind(i.numeric, c(names(attr(model$terms, "dataClasses"))[j], j, i))
b <- b + 1
(break)()
}
}
}
}
dd <- unlist(strsplit(substr(tt, 6, nchar(tt) - 1), "[,]"))
tim <- dd[1]
sts <- substr(dd[2], 2, nchar(dd[2]))
if (length(attr(model$terms, "term.labels")) == 1) {
input.data <<- data.frame(cbind(stt = NA, ti = NA, cov = NA), NO=NA)
names(input.data)[3] <<- paste(attr(model$terms, "term.labels"))
names(input.data)[1:2] <<- c(paste(sts), paste(tim))
} else {
data1 <- data[, neededVar]
input.data <<- cbind(stt = NA, ti = NA, data1[1, ], NO=NA)
names(input.data)[1:2] <<- c(paste(sts), paste(tim))
input.data[1, ] <<- NA
}
if (length(i.numeric) == 0) {
i.numeric <- matrix(ncol = 3)
i.numeric <- rbind(i.numeric, V1 = paste(tim))
i.numeric[dim(i.numeric)[1], 3] <- which(names(data) == i.numeric[dim(i.numeric)[1],1])
i.numeric <- rbind(i.numeric, V1 = paste(sts))
i.numeric[dim(i.numeric)[1], 3] <- which(names(data) == i.numeric[dim(i.numeric)[1], 1])
i.numeric <- i.numeric[-1, ]
} else {
i.numeric <- rbind(i.numeric, V1 = paste(tim))
i.numeric[dim(i.numeric)[1], 3] <- which(names(data) == i.numeric[dim(i.numeric)[1], 1])
i.numeric <- rbind(i.numeric, V1 = paste(sts))
i.numeric[dim(i.numeric)[1], 3] <- which(names(data) == i.numeric[dim(i.numeric)[1], 1])
}
nn <- nrow(i.numeric)
if (is.null(nn)) {
nn <- 0
}
nf <- nrow(i.factor)
if (is.null(nf)) {
nf <- 0
}
if (nf > 0) {
output$manySliders.f <- renderUI({
slide.bars <- list(lapply(1:nf, function(j) {
selectInput(paste("factor", j, sep = ""),
names(attr(model$terms, "dataClasses")[as.numeric(i.factor[j, 2])]),
model$xlevels[[as.numeric(i.factor[j, 2]) - as.numeric(i.factor[j, 4])]], multiple = FALSE)
}))
do.call(tagList, slide.bars)
})
}
if (nn > 1) {
output$manySliders.n <- renderUI({
if (covariate == "slider") {
if (nn > 2){
slide.bars <- list(lapply(1:(nn - 2), function(j) {
sliderInput(paste("numeric", j, sep = ""), i.numeric[j, 1],
min = floor(min(na.omit(data[, as.numeric(i.numeric[j, 3])]))),
max = ceiling(max(na.omit(data[, as.numeric(i.numeric[j, 3])]))),
value = mean(na.omit(data[, as.numeric(i.numeric[j, 3])])))
}), br(), checkboxInput("times", "Predicted Survival at this Follow Up:"),
conditionalPanel(condition = "input.times == true",
sliderInput(paste("numeric", (nn - 1), sep = ""), i.numeric[(nn - 1), 1],
min = floor(min(na.omit(data[, as.numeric(i.numeric[(nn - 1), 3])]))),
max = ceiling(max(na.omit(data[, as.numeric(i.numeric[(nn - 1), 3])]))),
value = mean(na.omit(data[, as.numeric(i.numeric[(nn - 1), 3])])))))
}
if (nn == 2){
slide.bars <- list(br(), checkboxInput("times", "Predicted Survival at this Follow Up:"),
conditionalPanel(condition = "input.times == true",
sliderInput(paste("numeric", (nn - 1), sep = ""), i.numeric[(nn - 1), 1],
min = floor(min(na.omit(data[, as.numeric(i.numeric[(nn - 1), 3])]))),
max = ceiling(max(na.omit(data[, as.numeric(i.numeric[(nn - 1), 3])]))),
value = mean(na.omit(data[, as.numeric(i.numeric[(nn - 1), 3])])))))
}
}
if (covariate == "numeric") {
if (nn > 2){
slide.bars <- list(lapply(1:(nn - 2), function(j) {
numericInput(paste("numeric", j, sep = ""), i.numeric[j, 1],
value = round(mean(na.omit(data[, as.numeric(i.numeric[j, 3])]))))
}), br(), checkboxInput("times", "Predicted Survival at this Follow Up:"),
conditionalPanel(condition = "input.times == true",
numericInput(paste("numeric", (nn - 1), sep = ""), i.numeric[(nn - 1), 1],
value = round(mean(na.omit(data[, as.numeric(i.numeric[(nn - 1), 3])]))))))
}
if (nn == 2){
slide.bars <- list(br(), checkboxInput("times", "Predicted Survival at this Follow Up:"),
conditionalPanel(condition = "input.times == true",
numericInput(paste("numeric", (nn - 1), sep = ""), i.numeric[(nn - 1), 1],
value = round(mean(na.omit(data[, as.numeric(i.numeric[(nn - 1), 3])]))))))
}
}
do.call(tagList, slide.bars)
})
}
a <- 0
old.d <- NULL
new.d <- reactive({
input$add
if (nf > 0) {
input.f <- vector("list", nf)
for (i in 1:nf) {
input.f[[i]] <- isolate({
input[[paste("factor", i, sep = "")]]
})
names(input.f)[i] <- i.factor[i, 1]
}
}
if (nn > 1) {
input.n <- vector("list", (nn - 1))
for (i in 1:(nn - 1)) {
input.n[[i]] <- isolate({
input[[paste("numeric", i, sep = "")]]
})
names(input.n)[i] <- i.numeric[i, 1]
}
}
if (nn == 0) {
out <- data.frame(do.call("cbind", input.f))
}
if (nf == 0) {
out <- data.frame(do.call("cbind", input.n))
}
if (nf > 0 & nn > 0) {
out <- data.frame(do.call("cbind", input.f), do.call("cbind", input.n))
}
if (a == 0) {
wher <- match(names(out), names(input.data)[-1])
out2 <- cbind(out[wher], NO=input$add)
input.data <<- rbind(input.data[-1], out2)
}
if (a > 0) {
wher <- match(names(out), names(input.data))
out2 <- cbind(out[wher], NO=input$add)
if (isTRUE(compare(old.d, out)) == FALSE) {
input.data <<- rbind(input.data, out2)
}
}
a <<- a + 1
out
})
p1 <- NULL
data2 <- reactive({
if (input$add == 0)
return(NULL)
if (input$add > 0) {
OUT <- isolate({
if (isTRUE(compare(old.d, new.d())) == FALSE) {
new.d <- cbind(stat = 1, new.d())
names(new.d)[1] <- paste(sts)
if (n.strata > 0) {
pred <- predict(model, newdata = new.d, se.fit = TRUE,
conf.int = clevel, type = "expected", reference = "strata")
}
if (n.strata == 0) {
pred <- predict(model, newdata = new.d, se.fit = TRUE,
conf.int = clevel, type = "expected")
}
upb <- exp(-(pred$fit - (qnorm(1 - (1 - clevel)/2) * pred$se.fit)))
if (upb > 1) {
upb <- 1
}
lwb <- exp(-(pred$fit + (qnorm(1 - (1 - clevel)/2) * pred$se.fit)))
if (ptype == "st") {
d.p <- data.frame(Prediction = exp(-pred$fit), Lower.bound = lwb,
Upper.bound = upb)
}
if (ptype == "1-st") {
d.p <- data.frame(Prediction = 1 - exp(-pred$fit), Lower.bound = 1 - upb,
Upper.bound = 1 - lwb)
}
old.d <<- new.d[,-1]
data.p <- cbind(d.p, counter = 1, NO=input$add)
p1 <<- rbind(p1, data.p)
p1$count <- seq(1, dim(p1)[1])
p1
} else {
p1$count <- seq(1, dim(p1)[1])
OUT <- p1
}
})
}
OUT
})
s.fr <- NULL
old.d2 <- NULL
b <- 1
St <- TRUE
if (n.strata > 0) {
sub.fit1 <- reactive({
nam <- NULL
aa <- 0
fit1 <- survfit(model, newdata = new.d())
l.s <- attr(model$terms, "specials")$strata
for (i in l.s) {
nam0 <- paste(new.d()[[which(i.factor[, 2] == i)]], sep = "")
if (aa == 0) {
nam <- paste(nam0)
}
if (aa > 0) {
nam <- paste(nam, ", ", nam0, sep = "")
}
aa <- aa + 1
}
sub.fit1 <- subset(as.data.frame(summary(fit1)[c(2:4,6:7)]), strata == nam)
return(sub.fit1)
})
}
dat.p <- reactive({
if (isTRUE(compare(old.d2, new.d())) == FALSE) {
fit1 <- survfit(model, newdata = new.d())
if (n.strata == 0) {
sff <- as.data.frame(summary(fit1)[c(2:4,6:7)])
sff <- cbind(sff, event=1-sff$surv, part = b)
if (sff$time[1] != 0){
sff2 <- sff[1, ]
sff2[1, ] <- NA
sff2$time[1] <- 0
sff2$n.risk[1] <- model$n
sff2$surv[1] <- 1
sff2$event[1] <- 0
sff2$part[1] <- sff$part[1]
s.f <- rbind(sff2, sff)
} else {
s.f <- sff
}
}
if (n.strata > 0) {
sff <- cbind(sub.fit1(), part = b)
sff <- cbind(sff, event=1-sff$surv)
if (sff$time[1] != 0) {
sff2 <- sff[1, ]
sff2[1, ] <- NA
sff2$time[1] <- 0
sff2$n.risk[1] <- sff[1,2]
sff2$surv[1] <- 1
sff2$event[1] <- 0
sff2$part[1] <- sff$part[1]
s.f <- rbind(sff2, sff)
} else {
s.f <- sff
}
s.f$n.risk <- s.f$n.risk/s.f$n.risk[1]
}
if (dim(s.f)[1] < 3) {
St <<- FALSE
stop("Error in data structure: There is not enough data in the current strata level")
}
s.fr <<- rbind(s.fr, s.f)
old.d2 <<- new.d()
b <<- b + 1
}
s.fr
})
output$plot <- renderPlot({
data2()
if (St == TRUE) {
if (input$add == 0)
return(NULL)
if (input$add > 0) {
if (input$trans == TRUE) {
if (ptype == "st") {
pl <- ggplot(data = dat.p()) +
geom_step(aes(x = time, y = surv, alpha = n.risk, group = part), color = coll[dat.p()$part]) +
ylim(0, 1) + xlim(0, max(dat.p()$time) * 1.05) +
labs(title = "Estimated Survival Probability", x = "Follow Up Time", y = "S(t)") + theme_bw() +
theme(text = element_text(face = "bold", size = 12), legend.position = "none",
plot.title = element_text(hjust = .5))
}
if (ptype == "1-st") {
pl <- ggplot(data = dat.p()) +
geom_step(aes(x = time, y = event, alpha = n.risk, group = part), color = coll[dat.p()$part]) +
ylim(0, 1) + xlim(0, max(dat.p()$time) * 1.05) +
labs(title = "Estimated Probability", x = "Follow Up Time", y = "F(t)") +
theme_bw() + theme(text = element_text(face = "bold", size = 12), legend.position = "none",
plot.title = element_text(hjust = .5))
}
}
if (input$trans == FALSE) {
if (ptype == "st") {
pl <- ggplot(data = dat.p()) +
geom_step(aes(x = time, y = surv, group = part), color = coll[dat.p()$part]) +
ylim(0, 1) + xlim(0, max(dat.p()$time) * 1.05) +
labs(title = "Estimated Survival Probability", x = "Follow Up Time", y = "S(t)") + theme_bw() +
theme(text = element_text(face = "bold", size = 12), legend.position = "none",
plot.title = element_text(hjust = .5))
}
if (ptype == "1-st") {
pl <- ggplot(data = dat.p()) +
geom_step(aes(x = time, y = event, group = part), color = coll[dat.p()$part]) +
ylim(0, 1) + xlim(0, max(dat.p()$time) * 1.05) +
labs(title = "Estimated Probability", x = "Follow Up Time", y = "F(t)") +
theme_bw() + theme(text = element_text(face = "bold", size = 12), legend.position = "none",
plot.title = element_text(hjust = .5))
}
}
}
print(pl)
}
if (St == FALSE) {
print("Restart the application")
}
})
output$plot2 <- renderPlotly({
if (input$add == 0)
return(NULL)
if (is.null(new.d()))
return(NULL)
lim <- c(0, 1)
yli <- c(0 - 0.5, 10 + 0.5)
PredictNO <- 0:(sum(data2()$counter) - 1)
in.d <- data.frame(input.data[-1,-dim(input.data)[2]])
xx=matrix(paste(names(in.d), ": ",t(in.d), sep=""), ncol=dim(in.d)[1])
text.cov=apply(xx,2,paste,collapse="<br />")
if (dim(input.data)[1] > 11)
yli <- c(dim(input.data)[1] - 11.5, dim(input.data)[1] - 0.5)
p <- ggplot(data = data2(), aes(x = Prediction, y = PredictNO, text = text.cov,
label = Prediction, label2 = Lower.bound, label3=Upper.bound)) +
geom_point(size = 2, colour = data2()$count, shape = 15) +
ylim(yli[1], yli[2]) + coord_cartesian(xlim = lim) +
geom_errorbarh(xmax = data2()$Upper.bound, xmin = data2()$Lower.bound,
size = 1.45, height = 0.4, colour = data2()$count) +
labs(title = paste(clevel * 100, "% ", "Confidence Interval for Survival Probability", sep = ""),
x = "Survival Probability", y = NULL) +
theme_bw() + theme(axis.text.y = element_blank(), text = element_text(face = "bold", size = 10))
if (ptype == "st") {
p <- p + labs(title = paste(clevel * 100, "% ", "Confidence Interval for Survival Probability", sep = ""),
x = "Survival Probability", y = NULL)
}
if (ptype == "1-st") {
p <- p + labs(title = paste(clevel * 100, "% ", "Confidence Interval for F(t)", sep = ""),
x = "Probability", y = NULL)
}
gp=ggplotly(p, tooltip = c("text","label","label2","label3"))
dat.p()
gp
})
output$data.pred <- renderPrint({
if (input$add > 0) {
if (nrow(data2() > 0)) {
di <- ncol(input.data)
data.p <- merge(input.data[-1, ], data2()[1:5], by="NO")
data.p <- data.p[, !(colnames(data.p) %in% c("NO", "counter"))]
stargazer(data.p, summary = FALSE, type = "text")
}
}
})
output$summary <- renderPrint({
if (m.summary == "raw"){
summary(model)
} else{
coef.c <- exp(model$coef)
ci.c <- exp(suppressMessages(confint(model, level = clevel)))
stargazer(model, coef = list(coef.c), ci.custom = list(ci.c), p.auto = F,
type = "text", omit.stat = c("LL", "ser", "f"), ci = TRUE, ci.level = clevel,
single.row = TRUE, title = paste("Cox model:", model$call[2], sep = " "))
}
})
}
)
)
}
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