Description Usage Arguments Details Value References Examples
Fit the marginal proportional hazards mixture cure (PHMC) model with the generalized estimating equations (GEE). GEE approach is generalized to the marginal PHMC model through the expectation-solution (ES) algorithm to account for the correlation among the cure statuses and the dependence among the failure times of uncured patients to improve the estimation efficiency.
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formula |
a formula expression, of the form |
cureform |
a formula expression, of the form |
data |
a data frame in which to interpret the variables named in the |
id |
a vector which identifies the clusters. The length of |
model |
specifies your model, it can be |
corstr |
a character string specifying the correlation structure. The following are permitted: |
Var |
If it is TRUE, the program returns Std.Error by the sandwich method. By default, |
stdz |
If it is TRUE, all the covariates in the |
boots |
If it is TRUE, the program returns Std.Error by the bootstrap method. By default, |
nboot |
the number of bootstrap samples. The default is |
esmax |
specifies the maximum iteration number. If the convergence criterion is not met, the ES iteration will be stopped after |
eps |
tolerance for convergence. The default is |
The marginal PHMC model is considered in this function. For cure rate, a logistic regression model is employed and the probability of being cured is given by (1+\exp(γ Z))^{(-1)}. For uncured subject, the failure time is modeled by either the parametric proportional hazards model with Weibull baseline distributions or the semiparametric proportional hazards model. A covariate can be used either in formula
or in cureform
or in both. The model parameters are estimated by the expectation-solution (ES) algorithm and the standard error estimates are obtained from sandwich variance formula based on Niu and Peng (2014) and Niu et al. (2018).
An object of class geecure
is returned. It can be examined by print.geecure()
.
Niu, Y. and Peng, Y. (2014) Marginal regression analysis of clustered failure time data with a cure fraction. Journal of Multivariate Analysis, 123: 129-142.
Niu, Y., Song, L., Liu, Y, and Peng, Y. (2018) Modeling clustered long-term survivors using marginal mixture cure model. Biometrical Journal, doi: 10.1002/bjmj.201700114.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | # Be patient, the following examples may take several minites on a faster computer.
# Example 1. Fit the marginal parametric PHMC model for the smoking cessation data.
data(smoking)
smoking$Time <- ifelse(smoking$Relapse == 0, smoking$Timept1,
(smoking$Timept1 + smoking$Timept2)/2)
# plot the KM survival curve of smoking cessation data
plot(survfit(Surv(Time, Relapse) ~ SexF + (SI.UC), data = smoking),
ylab = "Survival function", xlab = "Years", ylim = c(0.5, 1),
xlim = c(0, 6), lty = 1:4, col = 1:4)
legend(0.5, 0.63, c("SI/Male", "SI/Female", "UC/Female", "UC/Male"), cex = 1,
col = c(2, 4, 3, 1), lty = c(2, 4, 3, 1))
geesmokingind <- geecure(Surv(Time, Relapse) ~ SexF + Duration + SI.UC + F10Cigs +
SexF * SI.UC, cureform = ~ SexF + Duration + SI.UC + F10Cigs + SexF * SI.UC,
data = smoking, model = "para", id = smoking$Zip, corstr = "independence")
geesmokingexch <- geecure(Surv(Time, Relapse) ~ SexF + Duration + SI.UC + F10Cigs +
SexF * SI.UC, cureform = ~ SexF + Duration + SI.UC + F10Cigs + SexF * SI.UC,
data = smoking, model = "para", id = smoking$Zip, corstr = "exchangeable")
# Example 2. Fit the marginal semiparametric PHMC model for the bmt data.
data(bmt)
bmt$g <- factor(bmt$g, label = c("ALL", "AML low risk", "AML high risk"))
bmt$Z8 <- factor(bmt$Z8, label = c("Otherwise", "FAB"))
# plot the KM survival curve of bmt data
plot(survfit(Surv(T2, d3) ~ 1, data = bmt), xlab = "Days", ylab = "Survival Probability",
cex.lab = 1.7, cex.axis = 2, cex.main = 1.7, mark.time = TRUE)
geebmtind <- geecure(Surv(T2, d3) ~ factor(g) + Z8, cureform = ~ factor(g) + Z8,
data = bmt, model = "semi", id = bmt$Z9, corstr= "independence")
geebmtexch <- geecure(Surv(T2, d3) ~ factor(g) + Z8, cureform = ~ factor(g) + Z8,
data = bmt, model = "semi", id = bmt$Z9, corstr= "exchangeable",
stdz = TRUE, boots = TRUE)
# Example 3. Fit the marginal semiparametric PHMC model for the tonsil data.
data(tonsil)
tonsil<-tonsil[-c(141,136,159),]
tonsil$Sex <- ifelse(tonsil$Sex == 1, 0, 1)
tonsil$Cond <- ifelse(tonsil$Cond == 1, 0, 1)
tonsil$T <- ifelse(tonsil$T < 4, 0, 1)
# plot the KM survival curve of tonsil data
plot(survfit(Surv(Time, Status) ~ 1, data = tonsil), xlab = "Days", ylab = "Survival
Probability", cex.lab = 1.7, cex.axis = 2, cex.main = 1.7, mark.time = TRUE)
geetonsilind <- geecure2(Surv(Time, Status) ~ Sex + factor(Grade) + Age + Cond + T,
cureform = ~ Sex + factor(Grade) + Age + Cond + T, data = tonsil,
id = tonsil$Inst, corstr = "independence")
geetonsilexch <- geecure2(Surv(Time, Status) ~ Sex + factor(Grade) +Age + Cond + T,
cureform = ~ Sex + factor(Grade) + Age + Cond + T, data = tonsil,
id = tonsil$Inst, corstr = "exchangeable", stdz = TRUE, Var = FALSE)
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