I am working with some survival data with missing values.
I am using the mice package to do multiple imputation.
I have found code in this thread which handles pooling of the MI results:
https://stat.ethz.ch/pipermail/r-help/2007-May/132180.html
Now I would like to plot a survival curve using the pooled results.
Here is a reproducible example:
require(survival)
require(mice)
set.seed(2)
dt <- colon
fit <- coxph(Surv(time,etype)~rx + sex + age, data=colon)
dummy <- data.frame(sex=c(1,1,1),rx=c("Obs","Lev","Lev+5FU"),age=c(40,40,40))
plot(survfit(fit, newdata=dummy) )
# now create some missing values in the data
dt <- colon
dt$rx[sample(1:nrow(dt),50)] <- NA
dt$sex [sample(1:nrow(dt),50)] <- NA
dt$age[sample(1:nrow(dt),50)] <- NA
imp <-mice(dt)
fit.imp <- coxph.mids(Surv(time,etype)~rx + sex + age,imp)
# Note, this function is defined below...
imputed=summary.impute(pool.impute(fit.imp))
print(imputed)
# now, how to plot a survival curve with the pooled results ?
########## begin code from linked thread above
coxph.mids <- function (formula, data, ...) {
call <- match.call()
if (!is.mids(data)) stop("The data must have class mids")
analyses <- as.list(1:data$m)
for (i in 1:data$m) {
data.i <- complete(data, i)
analyses[[i]] <- coxph(formula, data = data.i, ...)
}
object <- list(call = call, call1 = data$call,
nmis = data$nmis, analyses = analyses)
return(object)
}
pool.impute <- function (object, method = "smallsample") {
if ((m <- length(object$analyses)) < 2)
stop("At least two imputations are needed for pooling.\n")
analyses <- object$analyses
k <- length(coef(analyses[[1]]))
names <- names(coef(analyses[[1]]))
qhat <- matrix(NA, nrow = m, ncol = k, dimnames = list(1:m,names))
u <- array(NA, dim = c(m, k, k),
dimnames = list(1:m, names, names))
for (i in 1:m) {
fit <- analyses[[i]]
qhat[i, ] <- coef(fit)
u[i, , ] <- vcov(fit)
}
qbar <- apply(qhat, 2, mean)
ubar <- apply(u, c(2, 3), mean)
e <- qhat - matrix(qbar, nrow = m, ncol = k, byrow = TRUE)
b <- (t(e) %*% e)/(m - 1)
t <- ubar + (1 + 1/m) * b
r <- (1 + 1/m) * diag(b/ubar)
f <- (1 + 1/m) * diag(b/t)
df <- (m - 1) * (1 + 1/r)^2
if (method == "smallsample") {
if( any( class(fit) == "coxph" ) ){
### this loop is the hack for survival analysis ###
status <- fit$y[ , 2]
n.events <- sum(status == max(status))
p <- length( coefficients( fit ) )
dfc <- n.events - p
} else {
dfc <- fit$df.residual
}
df <- dfc/((1 - (f/(m + 1)))/(1 - f) + dfc/df)
}
names(r) <- names(df) <- names(f) <- names
fit <- list(call = call, call1 = object$call, call2 = object$call1,
nmis = object$nmis, m = m, qhat = qhat, u = u,
qbar = qbar, ubar = ubar, b = b, t = t, r = r, df = df,
f = f)
return(fit)
}
summary.impute <- function(object){
if (!is.null(object$call1)){
cat("Call: ")
dput(object$call1)
}
est <- object$qbar
se <- sqrt(diag(object$t))
tval <- est/se
df <- object$df
pval <- 2 * pt(abs(tval), df, lower.tail = FALSE)
coefmat <- cbind(est, se, tval, pval)
colnames(coefmat) <- c("Estimate", "Std. Error",
"t value", "Pr(>|t|)")
cat("\nCoefficients:\n")
printCoefmat( coefmat, P.values=T, has.Pvalue=T, signif.legend=T )
cat("\nFraction of information about the coefficients
missing due to nonresponse:","\n")
print(object$f)
ans <- list( coefficients=coefmat, df=df,
call=object$call1, fracinfo.miss=object$f )
invisible( ans )
}
### end code from linked thread above
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