98 lines
3.6 KiB
R
98 lines
3.6 KiB
R
# A reprise of tt.R, using (time1, time2) data.
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library(survival)
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library(splines)
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aeq <- function(x, y) all.equal(as.vector(x), as.vector(y))
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# A contrived example for the tt function
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#
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mkdata <- function(n, beta) {
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age <- round(runif(n, 20, 60))
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x <- rbinom(n, 1, .5)
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futime <- rep(40, n) # everyone has 40 years of follow-up
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entry <- pmax(0, seq(-10, 30, length=n)) # 1/4 enter at 0
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entry <- round(entry)
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status <- rep(0, n)
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dtime <- runif(n/2, 1, 40) # 1/2 of them die
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dtime <- sort(dtime)
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# The risk is set to beta[1]*x + beta[2]* f(current_age)
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# where f= 0 up to age 40, rises linear to age 70, flat after that
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for (i in 1:length(dtime)) {
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atrisk <- (futime >= dtime[i] & entry < dtime[i])
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c.age <- age + dtime
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age2 <- pmin(30, pmax(0, c.age-40))
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xbeta <- beta[1]*x + beta[2]*age2
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# Select a death according to risk
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risk <- ifelse(atrisk, exp(xbeta), 0)
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dead <- sample(1:n, 1, prob=risk/sum(risk))
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futime[dead] <- dtime[i]
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status[dead] <- 1
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}
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out <- data.frame(time1= entry, time2=round(futime,1), status=status,
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age=age, x=x, risk=risk,
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casewt = sample(1:5, n, replace=TRUE),
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grp = sample(1:15, n, replace=TRUE), id= 1:n)
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subset(out, time1 < time2)
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}
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set.seed(1953) # a good year
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# Make n larger for the (time1, time2) case; more stress.
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tdata <- mkdata(250, c(log(1.5), 2/30)) # data set has many ties
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#tdata <- mkdata(100, c(log(1.5), 2/30)) # data set has many ties
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tdata$strat <- floor(tdata$grp/10)
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dtime <- sort(unique(tdata$time2[tdata$status==1]))
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data2 <- survSplit(Surv(time1, time2, status) ~., tdata, cut=dtime)
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data2$c.age <- data2$age + data2$time2 # current age
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# fit1 uses data at the event times, fit2$c.age might have a
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# wider range due to censorings. To make the two fits agree
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# fix the knots. I know a priori that 20 to 101 will cover it.
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ns2 <- function(x) ns(x, Boundary.knots=c(20, 101), knots=c(45, 60, 75))
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fit1 <- coxph(Surv(time1, time2, status)~ x + tt(age), tdata,
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tt= function(x, t, ...) ns2(x+t))
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fit2 <- coxph(Surv(time1, time2, status) ~ x + ns2(c.age), data2)
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aeq(coef(fit1), coef(fit2))
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aeq(vcov(fit1), vcov(fit2))
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#
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# Check that cluster, weight, and offset were correctly expanded
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#
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fit3a <- coxph(Surv(time1, time2, status)~ x + tt(age), tdata, weights=casewt,
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tt= function(x, t, ...) ns2(x+t), x=TRUE)
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fit3b <- coxph(Surv(time1, time2, status) ~ x + ns2(c.age), data2,
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weights=casewt)
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aeq(coef(fit3a), coef(fit3b))
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aeq(vcov(fit3a), vcov(fit3b))
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fit4a <- coxph(Surv(time1, time2, status)~ x + tt(age), tdata,
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tt= function(x, t, ...) ns2(x+t), cluster=grp)
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fit4b <- coxph(Surv(time1, time2, status) ~ x + ns2(c.age), data2,
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cluster=grp)
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fit4c <- coxph(Surv(time1, time2, status) ~ x + ns2(c.age) + cluster(grp),
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data2)
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aeq(coef(fit4a), coef(fit4b))
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aeq(vcov(fit4a), vcov(fit4b))
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aeq(coef(fit4a), coef(fit4c))
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aeq(vcov(fit4a), vcov(fit4c))
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fit5a <- coxph(Surv(time1, time2, status)~ x + tt(age) + offset(grp/10), tdata,
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tt= function(x, t, ...) ns2(x+t),)
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fit5b <- coxph(Surv(time1, time2, status) ~ x + ns2(c.age)+ offset(grp/10)
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, data=data2)
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aeq(coef(fit5a), coef(fit5b))
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aeq(vcov(fit5a), vcov(fit5b))
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# Check that strata is correct
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fit6a <- coxph(Surv(time1, time2, status) ~ x + tt(age) + strata(strat), tdata,
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tt = function(x, t, ...) (x+t)^2, x=TRUE)
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fit6b <- coxph(Surv(time1, time2, status) ~ x + I(c.age^2) +strata(strat), data2)
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aeq(coef(fit6a), coef(fit6b))
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aeq(vcov(fit6a), vcov(fit6b))
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