Section 2.1: Gastric cancer study
## ------------------------------------------
data("gastric", package = "coxphw")
head(gastric)
## id radiation time status
## 1 1 0 1 1
## 2 2 1 17 1
## 3 3 1 42 1
## 4 4 1 44 1
## 5 5 1 48 1
## 6 6 1 60 1
## time in years
gastric$yrs <- gastric$time / 365.25
nrow(gastric)
## [1] 90
## follow-up/observation time
survfit(Surv(yrs, abs(1 - status)) ~ 1, data = gastric)
## Call: survfit(formula = Surv(yrs, abs(1 - status)) ~ 1, data = gastric)
##
## n events median 0.95LCL 0.95UCL
## [1,] 90 11 4.34 4 NA
## descriptive analysis
gtable0 <- table(gastric$status, deparse.level = 2)
gtable0
## gastric$status
## 0 1
## 11 79
round(prop.table(gtable0) * 100, digits = 2)
## gastric$status
## 0 1
## 12.22 87.78
gtable1 <- table(gastric$radiation, gastric$status, deparse.level = 2)
addmargins(gtable1)
## gastric$status
## gastric$radiation 0 1 Sum
## 0 3 42 45
## 1 8 37 45
## Sum 11 79 90
round(prop.table(gtable1, margin = 1) * 100, digits = 2)
## gastric$status
## gastric$radiation 0 1
## 0 6.67 93.33
## 1 17.78 82.22
## check assumption of proportional hazards
gsurv <- survfit(Surv(yrs, status) ~ radiation, data = gastric)
summary(gsurv)
## Call: survfit(formula = Surv(yrs, status) ~ radiation, data = gastric)
##
## radiation=0
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.00274 45 1 0.9778 0.0220 0.9356 1.000
## 0.17248 44 1 0.9556 0.0307 0.8972 1.000
## 0.28747 43 1 0.9333 0.0372 0.8632 1.000
## 0.34223 42 1 0.9111 0.0424 0.8316 0.998
## 0.49829 41 1 0.8889 0.0468 0.8017 0.986
## 0.59138 40 1 0.8667 0.0507 0.7728 0.972
## 0.68446 39 1 0.8444 0.0540 0.7449 0.957
## 0.71732 38 1 0.8222 0.0570 0.7178 0.942
## 0.82409 37 2 0.7778 0.0620 0.6653 0.909
## 0.93634 35 1 0.7556 0.0641 0.6399 0.892
## 0.96920 34 1 0.7333 0.0659 0.6149 0.875
## 0.97467 33 1 0.7111 0.0676 0.5903 0.857
## 0.98015 32 1 0.6889 0.0690 0.5661 0.838
## 1.04038 31 1 0.6667 0.0703 0.5422 0.820
## 1.04860 30 2 0.6222 0.0723 0.4955 0.781
## 1.06229 28 1 0.6000 0.0730 0.4727 0.762
## 1.07871 27 1 0.5778 0.0736 0.4501 0.742
## 1.11704 26 1 0.5556 0.0741 0.4278 0.721
## 1.25941 25 1 0.5333 0.0744 0.4058 0.701
## 1.33881 24 1 0.5111 0.0745 0.3841 0.680
## 1.36619 23 1 0.4889 0.0745 0.3626 0.659
## 1.43190 22 1 0.4667 0.0744 0.3415 0.638
## 1.43463 21 1 0.4444 0.0741 0.3206 0.616
## 1.46475 20 1 0.4222 0.0736 0.3000 0.594
## 1.53867 19 1 0.4000 0.0730 0.2797 0.572
## 1.55784 18 1 0.3778 0.0723 0.2597 0.550
## 1.84805 17 1 0.3556 0.0714 0.2399 0.527
## 1.85079 16 1 0.3333 0.0703 0.2205 0.504
## 2.04791 15 1 0.3111 0.0690 0.2014 0.481
## 2.13005 14 1 0.2889 0.0676 0.1827 0.457
## 2.15195 13 1 0.2667 0.0659 0.1643 0.433
## 2.18207 12 1 0.2444 0.0641 0.1462 0.409
## 2.61465 11 1 0.2222 0.0620 0.1286 0.384
## 2.65024 10 1 0.2000 0.0596 0.1115 0.359
## 2.67488 9 1 0.1778 0.0570 0.0948 0.333
## 3.40862 8 1 0.1556 0.0540 0.0787 0.307
## 3.47981 7 1 0.1333 0.0507 0.0633 0.281
## 3.88775 6 1 0.1111 0.0468 0.0486 0.254
## 4.24641 3 1 0.0741 0.0435 0.0234 0.234
## 4.63792 1 1 0.0000 NaN NA NA
##
## radiation=1
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0465 45 1 0.978 0.0220 0.936 1.000
## 0.1150 44 1 0.956 0.0307 0.897 1.000
## 0.1205 43 1 0.933 0.0372 0.863 1.000
## 0.1314 42 1 0.911 0.0424 0.832 0.998
## 0.1643 41 1 0.889 0.0468 0.802 0.986
## 0.1971 40 1 0.867 0.0507 0.773 0.972
## 0.2026 39 1 0.844 0.0540 0.745 0.957
## 0.2601 38 1 0.822 0.0570 0.718 0.942
## 0.2820 37 1 0.800 0.0596 0.691 0.926
## 0.2957 36 1 0.778 0.0620 0.665 0.909
## 0.3340 35 1 0.756 0.0641 0.640 0.892
## 0.3943 34 1 0.733 0.0659 0.615 0.875
## 0.4572 33 1 0.711 0.0676 0.590 0.857
## 0.4654 32 1 0.689 0.0690 0.566 0.838
## 0.5010 31 1 0.667 0.0703 0.542 0.820
## 0.5065 30 1 0.644 0.0714 0.519 0.801
## 0.5284 29 1 0.622 0.0723 0.496 0.781
## 0.5339 28 1 0.600 0.0730 0.473 0.762
## 0.5394 27 1 0.578 0.0736 0.450 0.742
## 0.5695 26 1 0.556 0.0741 0.428 0.721
## 0.6407 25 1 0.533 0.0744 0.406 0.701
## 0.6434 24 1 0.511 0.0745 0.384 0.680
## 0.6954 23 1 0.489 0.0745 0.363 0.659
## 0.8405 22 1 0.467 0.0744 0.341 0.638
## 0.8624 21 1 0.444 0.0741 0.321 0.616
## 1.0979 20 1 0.422 0.0736 0.300 0.594
## 1.2183 19 1 0.400 0.0730 0.280 0.572
## 1.2704 18 1 0.378 0.0723 0.260 0.550
## 1.3251 17 1 0.356 0.0714 0.240 0.527
## 1.4456 16 1 0.333 0.0703 0.221 0.504
## 1.4839 15 1 0.311 0.0690 0.201 0.481
## 1.5524 14 1 0.289 0.0676 0.183 0.457
## 1.5797 13 1 0.267 0.0659 0.164 0.433
## 1.5880 12 1 0.244 0.0641 0.146 0.409
## 2.1766 11 1 0.222 0.0620 0.129 0.384
## 2.3409 10 1 0.200 0.0596 0.111 0.359
## 3.7399 6 1 0.167 0.0583 0.084 0.331
## plot of cumulative survival probabilities
if (pdfind) { pdf(file = "figure1A.pdf", width = 10.2, height = 5) }
par(oma = c(2, 2, 0.5, 0.5), mar = c(2, 2, 0, 0))
plot(gsurv, lty = 1:2, las = 1, lwd = 2)
mtext(side = 1, line = 2.5, text = "time (years)", cex = 1.2)
mtext(side = 2, line = 3, text = "survival distribution function", cex = 1.2)
legend("topright", title = "radiation", legend = c("no", "yes"),
lty = 1:2, inset = 0.05, bty = "n", cex = 1.4)
if (pdfind) { dev.off() }
## plots of scaled Schoenfeld residuals and test departure from proportional hazards
gfit1 <- coxph(Surv(yrs, status) ~ radiation + cluster(id), data = gastric, x = TRUE,
method = "breslow")
gfit1.ph <- cox.zph(fit = gfit1, transform = "km")
gfit1.ph
## chisq df p
## radiation 12.4 1 0.00042
## GLOBAL 12.4 1 0.00042
if (pdfind) { pdf(file = "figure1B.pdf", width = 5, height = 5) }
par(oma = c(2, 2, 0.5, 0.5), mar = c(2, 2, 0, 0))
plotcoxzph(x = gfit1.ph, wd = 2, limits = c(-3, 4.3))
abline(a = 0, b = 0, lty = 3)
mtext(side = 1, line = 2.5, cex = 1.2,
text = expression(paste("time (years, ", hat(F), "(t) transformation)")))
mtext(side = 2, line = 2.2, cex = 1.2,
text = expression(paste(hat(beta), "(t) for radiation")))
## add the linear fit
abline(lm(gfit1.ph$y ~ gfit1.ph$x)$coefficients, lty = 3, col = "red", lwd = 2)
if (pdfind) { dev.off() }
gfit1.ph2 <- cox.zph(fit = gfit1, transform = "identity")
if (pdfind) { pdf(file = "figure1C.pdf", width = 5.2, height = 5) }
par(oma = c(2, 2, 0.5, 0.5), mar = c(2, 2, 0, 2))
plotcoxzph(x = gfit1.ph2, wd = 2, limits = c(-3, 4.3))
mtext(side = 1, line = 2.5, text = "time (years)", cex = 1.2)
mtext(side = 2, line = 2.2, cex = 1.2,
text = expression(paste(hat(beta), "(t) for radiation")))
abline(a = 0, b = 0, lty = 3)
mtext(text = "radiation...", side = 4, line = 0.1, font = 3)
mtext(text = "protective", side = 4, line = 1, adj = 0, font = 3)
mtext(text = " harmful", side = 4, line = 1, adj = 1, font = 3)
if (pdfind) { dev.off() }
## ignore non-proportional hazards and apply a standard Cox proportional hazards model
summary(coxph(Surv(yrs, status) ~ radiation + cluster(id), data = gastric, x = TRUE,
method = "breslow"))
## Call:
## coxph(formula = Surv(yrs, status) ~ radiation, data = gastric,
## x = TRUE, method = "breslow", cluster = id)
##
## n= 90, number of events= 79
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## radiation 0.1415 1.1520 0.2263 0.2292 0.617 0.537
##
## exp(coef) exp(-coef) lower .95 upper .95
## radiation 1.152 0.8681 0.7351 1.805
##
## Concordance= 0.565 (se = 0.031 )
## Likelihood ratio test= 0.39 on 1 df, p=0.5
## Wald test = 0.38 on 1 df, p=0.5
## Score (logrank) test = 0.39 on 1 df, p=0.5, Robust = 0.37 p=0.5
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
## or equivalently
## coxphw(Surv(yrs, status) ~ radiation, data = gastric, template = "PH")
Section 2.2: Biofeedback therapy study
## ------------------------------------------
data("biofeedback", package = "coxphw")
head(biofeedback)
## id success thdur bfb theal log2heal
## 1 1 1 25 0 17 4.08746
## 2 2 1 5 1 20 4.32193
## 3 3 0 53 0 81 6.33985
## 4 4 0 100 1 135 7.07682
## 5 5 0 30 0 730 9.51175
## 6 6 1 89 0 15 3.90689
## descriptive analysis
nrow(biofeedback)
## [1] 33
btable0 <- table(biofeedback$bfb, deparse.level = 2)
btable0
## biofeedback$bfb
## 0 1
## 14 19
round(prop.table(btable0) * 100, digits = 2)
## biofeedback$bfb
## 0 1
## 42.42 57.58
## follow-up/observation time
## survfit(Surv(thdur, abs(1-success)) ~ 1, data = biofeedback)
survfit(Surv(thdur, success) ~ 1, data = biofeedback)
## Call: survfit(formula = Surv(thdur, success) ~ 1, data = biofeedback)
##
## n events median 0.95LCL 0.95UCL
## [1,] 33 23 25 21 89
btable1 <- table(biofeedback$bfb, biofeedback$success, deparse.level = 2)
addmargins(btable1)
## biofeedback$success
## biofeedback$bfb 0 1 Sum
## 0 4 10 14
## 1 6 13 19
## Sum 10 23 33
round(prop.table(btable1, margin = 1) * 100, digits = 2)
## biofeedback$success
## biofeedback$bfb 0 1
## 0 28.57 71.43
## 1 31.58 68.42
## hist(biofeedback$theal)
## hist(biofeedback$log2heal)
## Kaplan-Meier analysis
bsurv <- survfit(Surv(thdur, success) ~ bfb, data = biofeedback)
summary(bsurv)
## Call: survfit(formula = Surv(thdur, success) ~ bfb, data = biofeedback)
##
## bfb=0
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 9 14 1 0.929 0.0688 0.8030 1.000
## 18 13 1 0.857 0.0935 0.6921 1.000
## 24 12 1 0.786 0.1097 0.5977 1.000
## 25 11 2 0.643 0.1281 0.4351 0.950
## 32 8 1 0.562 0.1349 0.3515 0.900
## 58 6 1 0.469 0.1413 0.2596 0.846
## 84 5 1 0.375 0.1407 0.1797 0.783
## 85 4 1 0.281 0.1332 0.1112 0.711
## 89 3 1 0.188 0.1172 0.0551 0.639
##
## bfb=1
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 5 19 1 0.947 0.0512 0.852 1.000
## 7 18 1 0.895 0.0704 0.767 1.000
## 11 17 1 0.842 0.0837 0.693 1.000
## 13 16 1 0.789 0.0935 0.626 0.996
## 14 15 2 0.684 0.1066 0.504 0.929
## 15 13 1 0.632 0.1107 0.448 0.890
## 17 12 1 0.579 0.1133 0.395 0.850
## 20 11 1 0.526 0.1145 0.344 0.806
## 21 10 1 0.474 0.1145 0.295 0.761
## 22 9 1 0.421 0.1133 0.249 0.713
## 23 8 1 0.368 0.1107 0.204 0.664
## 33 6 1 0.307 0.1079 0.154 0.611
if (pdfind) { pdf(file = "figure2A.pdf", width = 10, height = 5) }
par(oma =c(2, 2, 0.5, 0.5), mar = c(2, 2, 0, 0))
plot(bsurv, fun = "event", lty = 1:2, lwd = 2, las = 1, ylim = c(0, 1))
mtext(side = 1, line = 2.5, text = "duration of therapy (days)", cex = 1.2)
mtext(side = 2, line = 3, text = "cumulative propability of rehabilitation", cex = 1.2)
legend("topleft", title = "biofeedback (bfb)", legend = c("no", "yes"), lty = 1:2,
inset = 0.05, bty = "n", cex = 1.4)
if (pdfind) { dev.off() }
bfit1 <- coxph(Surv(thdur, success) ~ bfb + log2heal + cluster(id), data = biofeedback,
x = TRUE, method = "breslow")
summary(bfit1)
## Call:
## coxph(formula = Surv(thdur, success) ~ bfb + log2heal, data = biofeedback,
## x = TRUE, method = "breslow", cluster = id)
##
## n= 33, number of events= 23
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## bfb 0.2700 1.3099 0.4273 0.3453 0.782 0.434
## log2heal -0.5267 0.5906 0.2543 0.3636 -1.448 0.148
##
## exp(coef) exp(-coef) lower .95 upper .95
## bfb 1.3099 0.7634 0.6658 2.577
## log2heal 0.5906 1.6933 0.2896 1.205
##
## Concordance= 0.665 (se = 0.061 )
## Likelihood ratio test= 7.19 on 2 df, p=0.03
## Wald test = 2.66 on 2 df, p=0.3
## Score (logrank) test = 5.16 on 2 df, p=0.08, Robust = 4.72 p=0.09
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
bfit1.ph <- cox.zph(bfit1, transform = "km")
bfit1.ph
## chisq df p
## bfb 7.44 1 0.0064
## log2heal 4.29 1 0.0384
## GLOBAL 11.10 2 0.0039
if (pdfind) { pdf(file = "figure2B.pdf", width = 5, height = 5) }
par(oma = c(2, 2, 0.5, 0.5), mar = c(2, 2, 0, 0))
plotcoxzph(x = bfit1.ph[1], wd = 2)
mtext(side = 1, line = 2.5, text = "duration of therapy (days)", cex = 1.2)
mtext(side = 2, line = 2.2, text = expression(hat(beta)), cex = 1.2)
abline(a = 0, b = 0, lty = 3)
abline(lm(bfit1.ph$y[, 1] ~ bfit1.ph$x)$coefficients, lty = 3, col = "red", lwd = 2)
legend("bottomleft", legend = "bfb", bty = "n", inset = 0.08, cex = 1.5)
if (pdfind) { dev.off() }
if (pdfind) { pdf(file = "figure2C.pdf", width = 5, height = 5) }
par(oma = c(2, 2, 0.5, 0.5), mar = c(2, 2, 0, 0))
plotcoxzph(x = bfit1.ph[2], wd = 2, limits = c(-4.5, 4))
mtext(side = 1, line = 2.5, text = "duration of therapy (days)", cex = 1.2)
mtext(side = 2, line = 2.2, text = expression(hat(beta)), cex = 1.2)
abline(a = 0, b = 0, lty = 3)
abline(lm(bfit1.ph$y[, 2] ~ bfit1.ph$x)$coefficients, lty = 3, col = "red", lwd = 2)
legend("topright", legend = "log2heal", bty = "n", inset = 0.08, cex = 1.5)
if (pdfind) { dev.off() }
Section 5: Simulation
simulation <- function(n1 = 100, n2 = 100, sim = 10, seed = 123,
type = c("ph", "nph1", "nph2", "nph3"), scalewei = NULL,
shapewei = NULL, beta = NULL, scaleexp = NULL, shapewei2 = NULL,
scalewei2 = NULL, shapegom = NULL, scalegom = NULL, scaleexpC,
admincens, npop = 10000, xmaxplot = NULL, addconstant = 1e-4)
{
## Simulate time-to-event data (following either an expoential, Weibuill or Gompertz
## distribution) with one binary explanatory variable, generate six versions of
## each simulated data set with differnt censoring patterns (no censoring, administrative
## censoring and loss-to-follow-up) and analyse these data sets with Cox regression
## and weighted Cox regression. Population-c is computed, as well.
##
## sim: number of simulations, 0 only population c is computed and plot is generated.
##
## type = "ph" : Weibull distributed distributions, proportional hazards
## "nph1" : exponential and Weibull distribution, non-proportional hazards
## "nph2" : exponential and Weibull distribution, non-proportional hazards
## "nph3" : exponential and Gompertz distribution, non-proportional hazards
##
## "ph" requires scalewei, shapewei and beta
## "nph1" requires scalewei, shapewei and scaleexp
## "nph2" requires scalewei, shapewei, scalewei2 and shapewei2
## "nph3" requires scaleexp, scalegom and shapegom
##
## scaleexpC and admincens: parameters for loss-to-follow-up and adminitrative censoring
##
## add.constant : this number will be added to all times to prevent survival times of
## exactly 0.
type <- match.arg(type)
if (type == "ph") {
stopifnot(!is.null(scalewei),!is.null(shapewei),!is.null(beta))
} else if (type == "nph1") {
stopifnot(!is.null(scalewei),!is.null(shapewei),!is.null(scaleexp))
} else if (type == "nph2") {
stopifnot(!is.null(scalewei),!is.null(shapewei),!is.null(scalewei2),!is.null(shapewei2))
} else if (type == "nph3") {
stopifnot(!is.null(scaleexp),!is.null(scalegom),!is.null(shapegom))
}
set.seed(seed)
## 1) compute population c
if (type != "ph") {
if (type == "nph1") {
integrandA <- function(x) { (scalewei * exp(-scalewei * x)) *
exp(-scalewei * x ^ scalewei) }
} else if (type == "nph2") {
integrandA <- function(x) { (scalewei2 * shapewei2 * x ^ (shapewei2 - 1) *
exp(-scalewei2 * x ^ shapewei2)) * exp(-scalewei *
x ^ shapewei) }
} else if (type == "nph3") {
integrandA <- function(x) { scaleexp * exp(-scaleexp * x) *
exp(scalegom / shapegom * (1 - exp(shapegom * x))) }
}
popc100 <- rep(c(integrate(integrandA, lower = 0, upper = Inf)$value,
integrate(integrandA, lower = 0, upper = admincens[1])$value,
integrate(integrandA, lower = 0, upper = admincens[2])$value) * 100,
each = 2)
} else { popc100 <- rep(exp(beta) / (1+exp(beta)), 6) * 100 }
if (sim == 0) { output <- list(results = NA, olist = NA, popc100 = popc100) }
## 2) Kaplan-Meier plot of scenario
xpop <- c(rep(0, npop / 2), rep(1, npop / 2))
u <- runif(n = npop, min = 0, max = 1)
if (type == "ph") {
time1pop <- (-log(u[1:(npop / 2)]) / (scalewei * exp(beta * 0))) ^ (1 / shapewei)
time2pop <- (-log(u[((npop / 2) + 1):npop]) / (scalewei * exp(beta * 1))) ^ (1 / shapewei)
} else if (type == "nph1") {
time1pop <- ((-log(u[1:(npop / 2)])) / scalewei) ^ (1 / shapewei)
time2pop <- -log(u[((npop / 2) + 1):npop]) / scaleexp
} else if (type == "nph2") {
time1pop <- ((-log(u[1:(npop / 2)])) / scalewei) ^ (1 / shapewei)
time2pop <- ((-log(u[((npop / 2) + 1):npop])) / scalewei2) ^ (1 / shapewei2)
} else if (type == "nph3") {
time1pop <- 1 / shapegom * log(1 - (shapegom * log(u[1:(npop / 2)])) / scalegom)
time2pop <- -log(u[((npop / 2) + 1):npop]) / scaleexp
}
time1pop <- time1pop + addconstant
time2pop <- time2pop + addconstant
datapop <- data.frame(cbind(time = c(time1pop, time2pop), event = 1, x = xpop))
fitpop <- coxph(Surv(time, event) ~ x, data = datapop)
if (is.null(xmaxplot)) { xmaxplot <- max(datapop$time) }
par(oma = c(2, 2, 0.5, 0.5), mar = c(2, 2, 0, 0))
plot(survfit(Surv(time, event) ~ x, data = datapop), lty = 1:2, lwd = 2,
las = 1, xlim = c(0, xmaxplot))
abline(v = admincens, col = "gray", lty = 2)
mtext(side = 1, line = 2.5, text = "time", cex = 1.2)
mtext(side = 2, line = 3, text = "survival distribution function",
cex = 1.2)
mtext(side = 3, line = -3, cex = 1.2, font = 2,
text = if (type == "ph") { "proportional hazards scenario" } else {
"non-proportional\nhazards scenario" } )
## 3) simulate data sets and analyse them
if (sim != 0) {
n <- n1 + n2
out <- data.frame(matrix(NA, nrow = sim, ncol = 7, dimnames = list(1:(sim),
c("cens", "cox_beta", "cox_c100", "wcox_beta", "wcox_c100",
"wilcox100", "prt0st1"))))
olist <- list(out = out, outC = out, out1 = out, outC1 = out, out2 = out, outC2 = out)
x <- c(rep(0, n1), rep(1, n2))
for (i in 1:sim) {
cat(paste(".", sep = ""))
## simulate data without censoring (data), type 1
u <- runif(n = n, min = 0, max = 1)
if (type == "ph") {
time1 <- (-log(u[1:n1]) / (scalewei * exp(beta * 0))) ^ (1 / shapewei)
time2 <- (-log(u[(n1 + 1):n]) / (scalewei * exp(beta * 1))) ^ (1 / shapewei)
} else if (type == "nph1") {
time1 <- ((-log(u[1:n1])) / scalewei) ^ (1 / shapewei)
time2 <- -log(u[(n1 + 1):n]) / scaleexp
} else if (type == "nph2") {
time1 <- ((-log(u[1:n1])) / scalewei) ^ (1 / shapewei)
time2 <- ((-log(u[(n1 + 1):n])) / scalewei2) ^ (1 / shapewei2)
} else if (type == "nph3") {
time1 <- 1 / shapegom * log(1 - (shapegom * log(u[1:n1])) / scalegom)
time2 <- -log(u[(n1 + 1):n]) / scaleexp
}
time1 <- time1 + addconstant
time2 <- time2 + addconstant
data <- data.frame(cbind(time = c(time1, time2), event = 1, x = x))
fit1 <- coxph(Surv(time, event) ~ x, data = data)
fit2 <-
coxphw(Surv(time, event) ~ x, data = data)
fit1zph <- cox.zph(fit = fit1, transform = "km")
eg <- expand.grid(time1, time2)
olist$out[i, ] <- c(
0,
fit1$coefficients, concord(fit1)[1],
fit2$coefficients, concord(fit2)[1],
wilcox.test(time ~ x, data = data, correct = FALSE)$statistic /
(n1 * n2),
1 - (sum(eg[, 1] < eg[, 2]) / nrow(eg)))
## follow-up distribution
timecens <- (-log(runif(n = nrow(data), min = 0, max = 1)) / scaleexpC) + addconstant
## data with censoring (dataC), type 2
dataC <- data
dataC$time[data$time > timecens] <- timecens[data$time > timecens]
dataC$event[data$time > timecens] <- 0
censC <- sum(dataC$event == 0) / n * 100
fit1C <- coxph(Surv(time, event) ~ x, data = dataC)
fit2C <- coxphw(Surv(time, event) ~ x, data = dataC)
dataC$id <- 1:nrow(dataC)
wilcoxC <- wilcox.test(time ~ x, data = dataC, correct = FALSE)$statistic
egC <- expand.grid(dataC$event[dataC$x == 0], dataC$event[dataC$x == 1])
olist$outC[i, ] <- c(censC,
fit1C$coefficients, concord(fit1C)[1],
fit2C$coefficients, concord(fit2C)[1],
wilcoxC / (length(dataC$x[dataC$x == 0]) *
length(dataC$x[dataC$x == 1])),
NA)
## data with administrative censoring 1 (data1), type 3
data1 <- data
data1$event[data$time > admincens[1]] <- 0
data1$time[data$time > admincens[1]] <- admincens[1]
cens1 <- sum(data1$event == 0) / n * 100
fit11 <- coxph(Surv(time, event) ~ x, data = data1)
fit21 <- coxphw(Surv(time, event) ~ x, data = data1)
fit11zph <- cox.zph(fit = fit11, transform = "km")
eg1 <- eg[!(eg[, 1] >= admincens[1] & eg[, 2] >= admincens[1]), ]
wilcox1 <- wilcox.test(time ~ x, data = data1, correct = FALSE)$statistic
olist$out1[i, ] <- c(cens1,
fit11$coefficients, concord(fit11)[1],
fit21$coefficients, concord(fit21)[1],
wilcox1 / (n1 * n2),
1 - ((sum(eg1[, 1] < eg1[, 2]) +
sum(eg[, 1] >= admincens[1] &
eg[, 2] >= admincens[1]) / 2) / nrow(eg)))
## data1 with censoring (datacens1), type 4
dataC1 <- data1
dataC1$time[data1$time > timecens] <- timecens[data1$time > timecens]
dataC1$event[data1$time > timecens] <- 0
censC1 <- sum(dataC1$event == 0) / n * 100
fit1C1 <- coxph(Surv(time, event) ~ x, data = dataC1)
fit2C1 <- coxphw(Surv(time, event) ~ x, data = dataC1)
wilcoxC1 <- wilcox.test(time ~ x, data = dataC1, correct = FALSE)$statistic
egC1 <- expand.grid(dataC1$event[dataC1$x == 0], dataC1$event[dataC1$x == 1])
olist$outC1[i, ] <- c(censC1,
fit1C1$coefficients, concord(fit1C1)[1],
fit2C1$coefficients, concord(fit2C1)[1],
wilcoxC1 / (length(dataC1$x[dataC1$x == 0]) *
length(dataC1$x[dataC1$x == 1])),
NA)
## data with administrative censoring 2 (data2), type 5
data2 <- data
data2$event[data$time > admincens[2]] <- 0
data2$time[data$time > admincens[2]] <- admincens[2]
cens2 <- sum(data2$event == 0) / n * 100
fit12 <- coxph(Surv(time, event) ~ x, data = data2)
fit22 <- coxphw(Surv(time, event) ~ x, data = data2)
fit12zph <- cox.zph(fit = fit12, transform = "km")
eg2 <- eg[!(eg[, 1] >= admincens[2] & eg[, 2] >= admincens[2]), ]
wilcox2 <- wilcox.test(time ~ x, data = data2, correct = FALSE)$statistic
olist$out2[i, ] <- c(cens2,
fit12$coefficients, concord(fit12)[1],
fit22$coefficients, concord(fit22)[1],
wilcox2 / (n1 * n2),
1 - ((sum(eg2[, 1] < eg2[, 2]) +
sum(eg[, 1] >= admincens[2] &
eg[, 2] >= admincens[2]) / 2) / nrow(eg)))
## data2 with censoring (datacens2), type 6
dataC2 <- data2
dataC2$time[data2$time > timecens] <- timecens[data2$time > timecens]
dataC2$event[data2$time > timecens] <- 0
censC2 <- sum(dataC2$event == 0) / n * 100
fit1C2 <- coxph(Surv(time, event) ~ x, data = dataC2)
fit2C2 <- coxphw(Surv(time, event) ~ x, data = dataC2)
wilcoxC2 <- wilcox.test(time ~ x, data = dataC2, correct = FALSE)$statistic
egC2 <- expand.grid(dataC2$event[dataC2$x == 0], dataC2$event[dataC2$x == 1])
olist$outC2[i, ] <- c(censC2,
fit1C2$coefficients, concord(fit1C2)[1],
fit2C2$coefficients, concord(fit2C2)[1],
wilcoxC2 / (length(dataC2$x[dataC2$x == 0]) *
length(dataC2$x[dataC2$x == 1])),
NA)
}
results <- matrix(NA, nrow = 6, ncol = 7, dimnames = list(c("No", "Loss-to-fup",
"Admin. 1", "Loss-to-fup & admin. 1", "Admin. 2", "Loss-to-fup & admin. 2"),
colnames(olist$out)))
for (j in 1:6) {
olist[[j]][, c("cox_c100", "wcox_c100", "wilcox100", "prt0st1")] <-
olist[[j]][, c("cox_c100", "wcox_c100", "wilcox100", "prt0st1")] * 100
r1 <- round(apply(olist[[j]], 2, mean), 3)
r2 <- round(apply(olist[[j]], 2, sd) / sqrt(sim), 3)
results[j, ] <- t(paste(r1, " (", r2, ")", sep = ""))
}
output <- list(results = results, olist = olist, popc100 = popc100)
}
invisible(output)
}
## Section 5. Simulation
if (runSimulation) {
## Figure 3 and Table 1
if (pdfind) { pdf("simph.pdf", width = 5, height = 5) }
sim1 <- simulation(n1 = 1000, n2 = 1000, sim = 2000, seed = 3460, type = "ph",
scalewei = 0.11, shapewei = 1.22, scaleexpC =0.06029,
beta = log(0.55/(1-0.55)), admincens = c(11.21083, 9.549136),
npop = 10000, xmaxplot = 23, addconstant = 1e-4)
if (pdfind) { dev.off() }
print(sim1$results[, 1:6])
print(round(sim1$popc100[1], 2)) # true population-c * 100
if (pdfind) { pdf("simnph.pdf", width = 5, height = 5) }
sim2 <- simulation(n1 = 1000, n2 = 1000, sim = 2000, seed = 3458, type ="nph3",
scaleexp = 0.35653, shapegom = 1.6, scalegom = 0.0228,
scaleexpC = 0.122, admincens = c(4.506223, 3.535000),
npop = 10000, xmaxplot = 6)
if (pdfind) { dev.off() }
print(sim2$results[, 1:6])
print(round(sim2$popc[1], 2)) # true population-c * 100
}
Section 6.1: Gastric cancer study
## prepare Table 2
models <- c("Ignoring non-proportional hazards *", "HR Cox regression",
"Estimating piecewise constant HRs *", "HR 1st year", "HR >1st year",
"Including a time-by-covariate interaction", "HR at 0.5 years", "HR at 1 year",
"HR at 2 years", "Weighted Cox regression", "average HR", "c'%")
Table2 <- data.frame(matrix(NA, nrow = length(models), ncol = 4, dimnames = list(models,
c("HR", "HRlower", "HRupper", "p"))))
## ignore non-proportional hazards and apply a Cox proportional hazards model
gfit2 <- coxphw(Surv(yrs, status) ~ radiation, data = gastric, template = "PH",
robust = TRUE)
## or equivalently
coxph(Surv(yrs, status) ~ radiation + cluster(id), data = gastric, x = TRUE,
method = "breslow")
## Call:
## coxph(formula = Surv(yrs, status) ~ radiation, data = gastric,
## x = TRUE, method = "breslow", cluster = id)
##
## coef exp(coef) se(coef) robust se z p
## radiation 0.1415 1.1520 0.2263 0.2292 0.617 0.537
##
## Likelihood ratio test=0.39 on 1 df, p=0.5325
## n= 90, number of events= 79
## extract estimates for Table 1: HR, 95% CI, p-value
Table2["HR Cox regression", ] <- c(exp(gfit2$coeff),
exp(confint(gfit2)),
summary(gfit2)$prob)
## coxphw(formula = Surv(yrs, status) ~ radiation, data = gastric,
## template = "PH", robust = TRUE)
##
## Model fitted by unweighted estimation (PH template)
##
## coef se(coef) exp(coef) lower 0.95 upper 0.95 z
## radiation 0.141495 0.2292141 1.151995 0.7350944 1.805335 0.6173052
## p
## radiation 0.5370334
##
## Wald Chi-square = 0.3810657 on 1 df p = 0.5370334 n = 90
##
## Covariance-Matrix:
## radiation
## radiation 0.05253909
##
## Generalized concordance probability: Estimates may be biased!
## concordance prob. lower 0.95 upper 0.95
## radiation 0.5353 0.4237 0.6435
## estimating piecewise constant hazard ratios (by two separate Cox models)
## (two time periods with equal number of events)
table(gastric$status)
##
## 0 1
## 11 79
## 79/2
nrow(subset(gastric, status == 1 & yrs < 1)) # breakpoint = 1 year
## [1] 39
## first time period
gastricp1 <- gastric
gastricp1$status[gastricp1$yrs > 1] <- 0
gastricp1$yrs[gastricp1$yrs > 1] <- 1
nrow(gastricp1)
## [1] 90
gtable0 <- table(gastricp1$status, deparse.level = 2)
gtable0
## gastricp1$status
## 0 1
## 51 39
round(prop.table(gtable0) * 100, digits = 2)
## gastricp1$status
## 0 1
## 56.67 43.33
gtable1 <- table(gastricp1$radiation, gastricp1$status, deparse.level = 2)
addmargins(gtable1)
## gastricp1$status
## gastricp1$radiation 0 1 Sum
## 0 31 14 45
## 1 20 25 45
## Sum 51 39 90
round(prop.table(gtable1, margin = 1) * 100, digits = 2)
## gastricp1$status
## gastricp1$radiation 0 1
## 0 68.89 31.11
## 1 44.44 55.56
gfit3 <- coxph(Surv(yrs, status) ~ radiation + cluster(id), data = gastricp1,
x = TRUE, method = "breslow")
gfit3.ph <- cox.zph(fit = gfit3, transform = "km")
gfit3.ph$table
## chisq df p
## radiation 2.836058 1 0.09217006
## GLOBAL 2.836058 1 0.09217006
## plot of scaled Schoenfeld residuals in the first time period
par(oma = c(2, 2, 0.5, 0.5), mar = c(2, 2, 0, 0))
plotcoxzph(x = gfit3.ph, wd = 2)
abline(a = 0, b = 0, lty = 3)
mtext(side = 1, line = 2.5, text = "time (years, KM-transformation)", cex = 1)
mtext(side = 2, line = 2.5, text = expression(paste(beta, "(t) for radiation")), cex = 1)
abline(lm(gfit3.ph$y ~ gfit3.ph$x)$coefficients, lty = 3, col = "red", lwd = 2)
## plot of cumulative survival probabilities
## gsurv2 <- survfit(Surv(yrs, status) ~ radiation, data = gastricp1)
## par(oma = c(2, 2, 0.5, 0.5), mar = c(2, 2, 0, 0))
## plot(gsurv2, lty = 1:2, las = 1, lwd = 2)
## mtext(side = 1, line = 2.5, text = "time (years)")
## mtext(side = 2, line = 3, text = "survival distribution function")
## legend("bottomleft", title = "radiation", legend = c("yes", "no"),
## lty = 2:1, inset = 0.02, bty = "n", cex = 1.2)
## Cox proportional hazards model for the first time period
gfit4 <- coxphw(Surv(yrs, status) ~ radiation, data = gastricp1, template = "PH")
summary(gfit4)
## coxphw(formula = Surv(yrs, status) ~ radiation, data = gastricp1,
## template = "PH")
##
## Model fitted by unweighted estimation (PH template)
##
## coef se(coef) exp(coef) lower 0.95 upper 0.95 z
## radiation 0.8774141 0.325826 2.404673 1.269733 4.55407 2.692892
## p
## radiation 0.007083531
##
## Wald Chi-square = 7.251665 on 1 df p = 0.007083531 n = 90
##
## Covariance-Matrix:
## radiation
## radiation 0.1061626
##
## Generalized concordance probability: Estimates may be biased!
## concordance prob. lower 0.95 upper 0.95
## radiation 0.7063 0.5594 0.82
Table2["HR 1st year", ] <- c(exp(gfit4$coeff),
exp(confint(gfit4)),
summary(gfit4, print = FALSE)$prob)
## or equivalently
## coxph(Surv(yrs, status) ~ radiation + cluster(id), data = gastricp1, method = "breslow")
## second time period
gastricp2 <- subset(gastric, yrs > 1)
nrow(gastricp2)
## [1] 51
gtable0 <- table(gastricp2$status, deparse.level = 2)
gtable0
## gastricp2$status
## 0 1
## 11 40
round(prop.table(gtable0) * 100, digits = 2)
## gastricp2$status
## 0 1
## 21.57 78.43
gtable1 <- table(gastricp2$radiation, gastricp2$status, deparse.level = 2)
addmargins(gtable1)
## gastricp2$status
## gastricp2$radiation 0 1 Sum
## 0 3 28 31
## 1 8 12 20
## Sum 11 40 51
round(prop.table(gtable1, margin = 1) * 100, digits = 2)
## gastricp2$status
## gastricp2$radiation 0 1
## 0 9.68 90.32
## 1 40.00 60.00
gfit5 <- coxph(Surv(yrs, status) ~ radiation + cluster(id), data = gastricp2, x = TRUE,
method = "breslow")
gfit5.ph <- cox.zph(fit = gfit5, transform = "km")
gfit5.ph$table
## chisq df p
## radiation 0.5706304 1 0.4500085
## GLOBAL 0.5706304 1 0.4500085
## plot of scaled Schoenfeld residuals for the second time period
par(oma = c(2, 2, 0.5, 0.5), mar = c(2, 2, 0, 0))
plotcoxzph(x = gfit5.ph, wd = 2)
abline(a = 0, b = 0, lty = 3)
mtext(side = 1, line = 2.5, text = "time (years, KM-transformation)", cex = 1)
mtext(side = 2, line = 2.5, text = expression(paste(beta, "(t) for radiation")), cex = 1)
abline(lm(gfit5.ph$y ~ gfit5.ph$x)$coefficients, lty = 3, col = "red", lwd = 2)
## plot of cumulative survival probabilities
## gsurv3 <- survfit(Surv(yrs, status) ~ radiation, data = gastricp2)
## par(oma = c(2, 2, 0.5, 0.5), mar = c(2, 2, 0, 0))
## plot(gsurv3, lty = 1:2, las = 1, lwd = 2, xlim=c(1,5))
## mtext(side = 1, line = 2.5, text = "time (years)")
## mtext(side = 2, line = 3, text = "survival distribution function")
## legend("bottomleft", title = "radiation", legend = c("yes", "no"),
## lty = 2:1, inset = 0.02, bty = "n", cex = 1.2)
## Cox proportional hazards model for the second time period
gfit6 <- coxphw(Surv(yrs, status) ~ radiation, data = gastricp2, template = "PH")
summary(gfit6)
## coxphw(formula = Surv(yrs, status) ~ radiation, data = gastricp2,
## template = "PH")
##
## Model fitted by unweighted estimation (PH template)
##
## coef se(coef) exp(coef) lower 0.95 upper 0.95 z
## radiation -0.6051688 0.3471071 0.5459823 0.2765161 1.078044 -1.743464
## p
## radiation 0.08125255
##
## Wald Chi-square = 3.039668 on 1 df p = 0.08125255 n = 51
##
## Covariance-Matrix:
## radiation
## radiation 0.1204833
##
## Generalized concordance probability: Estimates may be biased!
## concordance prob. lower 0.95 upper 0.95
## radiation 0.3532 0.2166 0.5188
Table2["HR >1st year", ] <- c(exp(gfit6$coeff),
exp(confint(gfit6)),
summary(gfit6, print = FALSE)$prob)
## or equivalently
## coxph(Surv(yrs, status) ~ radiation + cluster(id), data = gastricp2, method = "breslow")
## including a time-by-covariate interaction
fun <- function(t) as.numeric(t > 1)
gfit7 <- coxphw(Surv(yrs, status) ~ radiation + fun(yrs):radiation, data = gastric, template = "PH")
summary(gfit7)
## coxphw(formula = Surv(yrs, status) ~ radiation + fun(yrs):radiation,
## data = gastric, template = "PH")
##
## Model fitted by unweighted estimation (PH template)
##
## coef se(coef) exp(coef) lower 0.95 upper 0.95
## radiation 0.8774141 0.3258260 2.4046734 1.26973327 4.5540699
## fun(yrs):radiation -1.4825829 0.4758515 0.2270505 0.08934636 0.5769896
## z p
## radiation 2.692892 0.007083531
## fun(yrs):radiation -3.115642 0.001835452
##
## Wald Chi-square = 10.30011 on 2 df p = 0.005799087 n = 90
##
## Covariance-Matrix:
## radiation fun(yrs):radiation
## radiation 0.1061626 -0.1060570
## fun(yrs):radiation -0.1060570 0.2264347
##
## Generalized concordance probability: Estimates may be biased!
## concordance prob. lower 0.95 upper 0.95
## radiation 0.7063 0.5594 0.8200
## fun(yrs):radiation 0.1850 0.0820 0.3659
## 2.4046734 * 0.2270505
## exp(0.8774141 - 1.4825829)
## or equivalently
summary(coxph(Surv(yrs, status) ~ radiation + tt(radiation) + cluster(id), data = gastric,
tt = function(x, t, ...) x * (t > 1), method = "breslow"))
## Call:
## coxph(formula = Surv(yrs, status) ~ radiation + tt(radiation),
## data = gastric, tt = function(x, t, ...) x * (t > 1), method = "breslow",
## cluster = id)
##
## n= 90, number of events= 79
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## radiation 0.8774 2.4047 0.3351 0.3258 2.693 0.00708 **
## tt(radiation) -1.4826 0.2271 0.4816 0.4759 -3.116 0.00184 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## radiation 2.4047 0.4159 1.26973 4.554
## tt(radiation) 0.2271 4.4043 0.08935 0.577
##
## Concordance= 0.6 (se = 0.029 )
## Likelihood ratio test= 10.49 on 2 df, p=0.005
## Wald test = 10.3 on 2 df, p=0.006
## Score (logrank) test = 10.45 on 2 df, p=0.005, Robust = 10.73 p=0.005
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
## extended Cox model - assume a linear time-dependent effect
fit1 <- coxphw(Surv(yrs, status) ~ radiation + yrs:radiation, data = gastric, template = "PH")
summary(fit1)
## coxphw(formula = Surv(yrs, status) ~ radiation + yrs:radiation,
## data = gastric, template = "PH")
##
## Model fitted by unweighted estimation (PH template)
##
## coef se(coef) exp(coef) lower 0.95 upper 0.95 z
## radiation 1.2699606 0.4334889 3.5607124 1.5224762 8.3276657 2.929627
## yrs:radiation -0.9652886 0.3409321 0.3808733 0.1952444 0.7429892 -2.831322
## p
## radiation 0.003393692
## yrs:radiation 0.004635608
##
## Wald Chi-square = 9.047941 on 2 df p = 0.01084588 n = 90
##
## Covariance-Matrix:
## radiation yrs:radiation
## radiation 0.1879126 -0.1241715
## yrs:radiation -0.1241715 0.1162347
##
## Generalized concordance probability: Estimates may be biased!
## concordance prob. lower 0.95 upper 0.95
## radiation 0.7807 0.6036 0.8928
## yrs:radiation 0.2758 0.1634 0.4263
## or equivalently
coxph(Surv(yrs, status) ~ radiation + tt(radiation) + cluster(id), tt = function(x,t, ...) x * t,
data = gastric, method = "breslow")
## Call:
## coxph(formula = Surv(yrs, status) ~ radiation + tt(radiation),
## data = gastric, tt = function(x, t, ...) x * t, method = "breslow",
## cluster = id)
##
## coef exp(coef) se(coef) robust se z p
## radiation 1.2700 3.5607 0.4186 0.4335 2.930 0.00339
## tt(radiation) -0.9653 0.3809 0.3177 0.3409 -2.831 0.00464
##
## Likelihood ratio test=13.47 on 2 df, p=0.001188
## n= 90, number of events= 79
## extract HR at 0.5, 1 and 2 years
fit1est <- predict(fit1, type = "slice.time", x = "yrs", z = "radiation", newx = c(0.5, 1, 2),
exp = TRUE, verbose = TRUE, pval = TRUE)
## yrs HR HR lower 0.95 HR upper 0.95 p
## 1 0.5 2.1975 1.2096 3.9924 0.0098
## 2 1.0 1.3562 0.8536 2.1547 0.1971
## 3 2.0 0.5165 0.2381 1.1207 0.0946
Table2[c("HR at 0.5 years", "HR at 1 year",
"HR at 2 years"), ] <- cbind(fit1est$estimates[, "HR"],
fit1est$estimates[, "HR lower 0.95"],
fit1est$estimates[, "HR upper 0.95"],
fit1est$estimates[, "p"])
## visualize the estimated linear time-dependent effect
fit1est2 <- predict(fit1, type = "slice.time", x = "yrs", z = "radiation",
newx = seq(from = 0.1, to = 3, by = 0.1))
if (pdfind) { pdf("figure3.pdf", width = 7, height = 5) }
par(oma = c(2, 2, 0.5, 0.5), mar=c(2, 2, 0, 0))
plot(fit1est2, addci = TRUE)
mtext(side = 1, line = 2.5, text = "time (yrs)", cex = 1.3)
mtext(side = 2, line = 2.3, text = expression(paste(hat(beta), "(t) for radiation")),
cex = 1.3)
if (pdfind) { dev.off() }
## extended Cox model - assume a log-linear time-dependent effect
gfit8 <- coxphw(Surv(yrs, status) ~ radiation + log(yrs):radiation, data = gastric,
template = "PH")
summary(gfit8)
## coxphw(formula = Surv(yrs, status) ~ radiation + log(yrs):radiation,
## data = gastric, template = "PH")
##
## Model fitted by unweighted estimation (PH template)
##
## coef se(coef) exp(coef) lower 0.95 upper 0.95
## radiation 0.03766302 0.2367992 1.0383813 0.6528193 1.651660
## log(yrs):radiation -0.66924556 0.4821178 0.5120948 0.1990540 1.317437
## z p
## radiation 0.1590504 0.8736291
## log(yrs):radiation -1.3881370 0.1650953
##
## Wald Chi-square = 1.97312 on 2 df p = 0.372857 n = 90
##
## Covariance-Matrix:
## radiation log(yrs):radiation
## radiation 0.056073853 0.004581723
## log(yrs):radiation 0.004581723 0.232437577
##
## Generalized concordance probability: Estimates may be biased!
## concordance prob. lower 0.95 upper 0.95
## radiation 0.5094 0.395 0.6229
## log(yrs):radiation 0.3387 0.166 0.5685
## or equivalently
coxph(Surv(yrs, status) ~ radiation + tt(radiation) + cluster(id),
tt = function(x, t, ...) x * log(t), data = gastric, method = "breslow")
## Call:
## coxph(formula = Surv(yrs, status) ~ radiation + tt(radiation),
## data = gastric, tt = function(x, t, ...) x * log(t), method = "breslow",
## cluster = id)
##
## coef exp(coef) se(coef) robust se z p
## radiation 0.03766 1.03838 0.23962 0.23680 0.159 0.874
## tt(radiation) -0.66925 0.51209 0.27299 0.48212 -1.388 0.165
##
## Likelihood ratio test=8.02 on 2 df, p=0.01809
## n= 90, number of events= 79
## weighted Cox regression
fit2 <- coxphw(Surv(yrs, status) ~ radiation, data = gastric, template = "AHR")
summary(fit2)
## coxphw(formula = Surv(yrs, status) ~ radiation, data = gastric,
## template = "AHR")
##
## Model fitted by weighted estimation (AHR template)
##
## coef se(coef) exp(coef) lower 0.95 upper 0.95 z
## radiation 0.4625051 0.2387432 1.588047 0.9945916 2.535608 1.937249
## p
## radiation 0.05271492
##
## Wald Chi-square = 3.752934 on 1 df p = 0.05271492 n = 90
##
## Covariance-Matrix:
## radiation
## radiation 0.05699834
##
## Generalized concordance probability:
## concordance prob. lower 0.95 upper 0.95
## radiation 0.6136 0.4986 0.7172
## weighted Cox regression with truncation of weights
gfit9 <- coxphw(Surv(yrs, status) ~ radiation, data = gastric, template = "AHR",
trunc.weights = 0.95)
summary(gfit9)
## coxphw(formula = Surv(yrs, status) ~ radiation, data = gastric,
## template = "AHR", trunc.weights = 0.95)
##
## Model fitted by weighted estimation (AHR template)
##
## coef se(coef) exp(coef) lower 0.95 upper 0.95 z
## radiation 0.4622282 0.2384041 1.587608 0.9949774 2.533221 1.938843
## p
## radiation 0.05252042
##
## Wald Chi-square = 3.759113 on 1 df p = 0.05252042 n = 90
##
## Covariance-Matrix:
## radiation
## radiation 0.05683651
##
## Generalized concordance probability:
## concordance prob. lower 0.95 upper 0.95
## radiation 0.6135 0.4987 0.717
if (pdfind) { pdf(file = "figure4.pdf", width = 6, height = 5) }
par(oma = c(2, 2, 0.5, 0.5), mar = c(2, 2, 0, 0))
plot(x = gfit9, las = 1, lwd = 2)
mtext(side = 1, line = 2.5, text = "time (years)")
mtext(side = 2, line = 2.5, text = "weight")
if (pdfind) { dev.off() }
## range of normalized totatl weights
range(gfit9$w.matrix[, "w"])
## [1] 0.3134808 1.6534706
Table2["average HR", ] <- cbind(exp(gfit9$coeff),
exp(confint(gfit9)),
summary(gfit9, print = FALSE)$prob)
Table2["c'%", ] <- c(as.vector(concord(gfit9)), summary(gfit9, print = FALSE)$prob)
## finish Table 1
Table2["c'%", 1:3] <- 100 * Table2["c'%", 1:3]
Table2 <- round(Table2, digits = 3)
Table2[, 1] <- paste(Table2[, 1], " (", Table2[, 2], "-", Table2[, 3], ")", sep = "")
Table2[, 2] <- Table2[, 4]
Table2 <- Table2[, 1:2]
dimnames(Table2)[[2]] <- c("Estimate (95% CI)", "p")
Table2
## Estimate (95% CI) p
## Ignoring non-proportional hazards * NA (NA-NA) NA
## HR Cox regression 1.152 (0.735-1.805) 0.537
## Estimating piecewise constant HRs * NA (NA-NA) NA
## HR 1st year 2.405 (1.27-4.554) 0.007
## HR >1st year 0.546 (0.277-1.078) 0.081
## Including a time-by-covariate interaction NA (NA-NA) NA
## HR at 0.5 years 2.197 (1.21-3.992) 0.010
## HR at 1 year 1.356 (0.854-2.155) 0.197
## HR at 2 years 0.517 (0.238-1.121) 0.095
## Weighted Cox regression NA (NA-NA) NA
## average HR 1.588 (0.995-2.533) 0.053
## c'% 61.35 (49.87-71.7) 0.053
Section 6.2: Biofeedback therapy study
## ignore non-proportional hazards and apply a Cox model
## (use breslow weights to make it directly comparable to coxphw)
bfit2 <- coxphw(Surv(thdur, success) ~ bfb + log2heal, data = biofeedback, template = "PH")
summary(bfit2)
## coxphw(formula = Surv(thdur, success) ~ bfb + log2heal, data = biofeedback,
## template = "PH")
##
## Model fitted by unweighted estimation (PH template)
##
## coef se(coef) exp(coef) lower 0.95 upper 0.95 z
## bfb 0.2699762 0.3453073 1.3099332 0.6657683 2.577361 0.7818433
## log2heal -0.5266649 0.3636448 0.5905713 0.2895592 1.204502 -1.4482949
## p
## bfb 0.4343067
## log2heal 0.1475346
##
## Wald Chi-square = 2.657646 on 2 df p = 0.2647887 n = 33
##
## Covariance-Matrix:
## bfb log2heal
## bfb 0.119237110 -0.002917929
## log2heal -0.002917929 0.132237551
##
## Generalized concordance probability: Estimates may be biased!
## concordance prob. lower 0.95 upper 0.95
## bfb 0.5671 0.3997 0.7205
## log2heal 0.3713 0.2245 0.5464
## or equivalently
coxph(Surv(thdur, success) ~ bfb + log2heal + cluster(id), data = biofeedback,
x = TRUE, method = "breslow")
## Call:
## coxph(formula = Surv(thdur, success) ~ bfb + log2heal, data = biofeedback,
## x = TRUE, method = "breslow", cluster = id)
##
## coef exp(coef) se(coef) robust se z p
## bfb 0.2700 1.3099 0.4273 0.3453 0.782 0.434
## log2heal -0.5267 0.5906 0.2543 0.3636 -1.448 0.148
##
## Likelihood ratio test=7.19 on 2 df, p=0.02753
## n= 33, number of events= 23
## two stage estimation
stage1 <- coxph(Surv(thdur, success) ~ strata(bfb) + log2heal + tt(log2heal) + cluster(id),
tt = function(x, t, ...) x * log(t), data = biofeedback, method = "breslow")
summary(stage1)
## Call:
## coxph(formula = Surv(thdur, success) ~ strata(bfb) + log2heal +
## tt(log2heal), data = biofeedback, tt = function(x, t, ...) x *
## log(t), method = "breslow", cluster = id)
##
## n= 33, number of events= 23
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## log2heal 0.7368 2.0892 0.9008 0.3797 1.940 0.05233 .
## tt(log2heal) -0.4153 0.6602 0.3257 0.1490 -2.786 0.00533 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## log2heal 2.0892 0.4787 0.9926 4.3971
## tt(log2heal) 0.6602 1.5148 0.4929 0.8841
##
## Concordance= 0.664 (se = 0.063 )
## Likelihood ratio test= 8.53 on 2 df, p=0.01
## Wald test = 7.84 on 2 df, p=0.02
## Score (logrank) test = 6.59 on 2 df, p=0.04, Robust = 7.71 p=0.02
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
## for comparison linear time-dependent effect
coxph(Surv(thdur, success) ~ strata(bfb) + log2heal + tt(log2heal) + cluster(id),
tt = function(x, t, ...) x * t, data = biofeedback, method = "breslow")
## Call:
## coxph(formula = Surv(thdur, success) ~ strata(bfb) + log2heal +
## tt(log2heal), data = biofeedback, tt = function(x, t, ...) x *
## t, method = "breslow", cluster = id)
##
## coef exp(coef) se(coef) robust se z p
## log2heal -0.02130 0.97892 0.45270 0.43296 -0.049 0.961
## tt(log2heal) -0.01957 0.98062 0.02218 0.01549 -1.263 0.206
##
## Likelihood ratio test=8.64 on 2 df, p=0.01331
## n= 33, number of events= 23
stage2 <- coxphw(Surv(thdur, success) ~ bfb + log2heal + log(thdur):log2heal, data = biofeedback,
template = "AHR", betafix = c(NA, coef(stage1)))
summary(stage2)
## coxphw(formula = Surv(thdur, success) ~ bfb + log2heal + log(thdur):log2heal,
## data = biofeedback, template = "AHR", betafix = c(NA, coef(stage1)))
##
## Model fitted by weighted estimation (AHR template)
##
## coef se(coef) exp(coef) lower 0.95 upper 0.95
## bfb 0.5967993 0.3732872 1.8162961 0.8738643 3.775107
## log2heal 0.7367590 NA 2.0891536 NA NA
## log(thdur):log2heal -0.4152653 NA 0.6601651 NA NA
## z p
## bfb 1.598767 0.1098724
## log2heal NA NA
## log(thdur):log2heal NA NA
##
## Wald Chi-square = 2.556056 on 1 df p = 0.1098724 n = 33 (based on: bfb )
##
## Covariance-Matrix:
## [1] 0.1393434
##
## Generalized concordance probability:
## concordance prob. lower 0.95 upper 0.95
## bfb 0.6449 0.4663 0.7906
## log2heal 0.6763 NA NA
## log(thdur):log2heal 0.3977 NA NA
if (pdfind) { pdf(file = "figure5.pdf", width = 6, height = 5) }
par(oma = c(2, 2, 0.5, 0.5), mar = c(2, 2, 0, 0))
plot(x = stage2, las = 1, legendxy = c(45, 1.15), lwd = 2)
mtext(side = 1, line = 2.5, text = "treatment duration (days)")
mtext(side = 2, line = 2.5, text = "weight")
if (pdfind) { dev.off() }