# rhDNase Data Randomized trial of rhDNase for treatment of cystic fibrosis data ```r data(dnase) ``` ## Format A data frame with 767 observations on the following 6 variables. - **`id`**: subject id - **`rx`**: treatment arm: 0 = placebo, 1 = rhDNase - **`fev`**: forced expirotary volume, a measure of lung capacity - **`futime`**: follow time - **`iv1`**: IV start time - **`iv2`**: IV stop time ## Details During an exacerbation, patients received intravenous (IV) antibiotics and were considered unsusceptible until seven exacerbation-free days beyond the end of IV therapy. A few subjects were infected at the time of enrollment, for instance a subject has a first infection interval of -21 to 7. We do not count this first infection as an "event", and the subject first enters the risk set at day 7. ## Source Therneau and Grambsch (2000). Modeling Survival Data: Extending the Cox model. Springer. http://www.mayo.edu/hsr/people/therneau/book/data/dnase.html ## References Yan and Fine (2008). Analysis of Episodic Data with Application to Recurrent Pulmonary Exacerbations in Cystic Fibrosis Patients. JASA. ## Examples ```r ## This example steps through how to set up for the tpr function. ## Three objects are needed: ## 1) response process (an object of "lgtdl") ## 2) data availability process (an object of "lgtdl") ## 3) a time-independent covariate matrix data(dnase) ## extracting the unique id and subject level information dat <- unique(dnase[,c("id", "futime", "fev", "rx")]) ## construct temporal process response for recurrent enent rec <- lapply(split(dnase[,c("id", "iv1", "futime")], dnase$id), function(x) { v <- x$iv1 maxfu <- max(x$futime) ## iv1 may be negative!!! if (is.na(v[1])) c(0, maxfu + 1.0) else if (v[1] < 0) c(v[1] - 1, v[!is.na(v)], maxfu + 1.0) else c(0, v[!is.na(v)], maxfu + 1.0) }) yrec <- lapply(rec, function(x) { dat <- data.frame(time=x, cov=1:length(x)-1) len <- length(x) dat$cov[len] <- dat$cov[len - 1] as.lgtdl(dat) }) ## construct temporal process response for accumulative days exacerbation do1.acc <- function(x) { gap <- x$iv2 - x$iv1 + 1 if (all(is.na(gap))) yy <- tt <- NULL else { gap <- na.omit(gap) yy <- cumsum(rep(1, sum(gap))) tt <- unlist(sapply(1:length(gap), function(i) seq(x$iv1[i], x$iv2[i], by=1.0))) } yy <- c(0, yy, rev(yy)[1]) if (!is.null(tt[1]) && tt[1] < 0) tt <- c(tt[1] - 1, tt, max(x$futime) + 1.0) else tt <- c(0, tt, max(x$futime) + 1.0) as.lgtdl(data.frame(time=tt, cov=yy)) } yacc <- lapply(split(dnase[,c("id", "iv1", "iv2", "futime")], dnase$id), do1.acc) ## construct data availability (or at risk) indicator process tu <- max(dat$futime) + 0.001 rt <- lapply(1:nrow(dat), function(i) { x <- dat[i, "futime"] time <- c(0, x, tu) cov <- c(1, 0, 0) as.lgtdl(data.frame(time=time, cov=cov)) }) ## time-independent covariate matrix xmat <- model.matrix(~ rx + fev, data=dat) ## time-window in days tlim <- c(10, 168) good <- unlist(lapply(yrec, function(x) x$time[1] == 0)) ## fully functional temporal process regression ## for recurrent event m.rec <- tpr(yrec, rt, xmat[,1:3], list(), xmat[,-(1:3),drop=FALSE], list(), tis=10:160, w = rep(1, 151), family = poisson(), evstr = list(link = 5, v = 3)) par(mfrow=c(1,3), mgp=c(2,1,0), mar=c(4,2,1,0), oma=c(0,2,0,0)) for(i in 1:3) ci.plot(m.rec$tis, m.rec$alpha[,i], sqrt(m.rec$valpha[,i])) ## hypothesis test of significance ## integral test, covariate index 2 and 3 sig.test.int.ff(m.rec, idx=2:3, ncut=2) sig.test.boots.ff(m.rec, idx=2:3, nsim=1000) ## constant fit cfit <- cst.fit.ff(m.rec, idx=2:3) ## goodness-of-fit test for constant fit gof.test.int.ff(m.rec, idx=2:3, ncut=2) gof.test.boots.ff(m.rec, idx=2:3, nsim=1000) ## for cumulative days in exacerbation m.acc <- tpr(yacc, rt, xmat[,1:3], list(), xmat[,-(1:3),drop=FALSE], list(), tis=10:160, w = rep(1, 151), family = gaussian(), evstr = list(link = 1, v = 1)) par(mfrow=c(1,3), mgp=c(2,1,0), mar=c(4,2,1,0), oma=c(0,2,0,0)) for(i in 1:3) ci.plot(m.acc$tis, m.acc$alpha[,i], sqrt(m.acc$valpha[,i])) ```

dnase function