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 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
