ipw.log.rank: Log-Rank Test for Adjusted Survival Curves.

In RISCA: Causal Inference and Prediction in Cohort-Based Analyses

Log-Rank Test for Adjusted Survival Curves.

Description

The user enters individual survival data and the weights previously calculated (by using logistic regression for instance). The usual log-rank test is adapted to the corresponding adjusted survival curves.

Usage

ipw.log.rank(times, failures, variable, weights)

Arguments

times	A numeric vector with the follow up times.
failures	A numeric vector with the event indicator (0=right censored, 1=event).
variable	A numeric vector with the binary variable under interest (only two groups).
weights	The weights for correcting the contribution of each individual. By default, the weights are all equaled to 1 and the survival curves correspond to the usual Kaplan-Meier estimator.

Details

For instance, the weights may be equal to 1/p, where p is the estimated probability of the individual to be in its group. The probabilities p are often estimated by a logistic regression in which the dependent binary variable is the group. The possible confounding factors are the explanatory variables of this model.

Value

statistic	The value of the log-rank statistic.
p.value	The p-value associated to the previous log-rank statistic.

Author(s)

Yohann Foucher <[e-mail]>

Jun Xie <[e-mail]>

Florant Le Borgne <[e-mail]>

References

Le Borgne et al. Comparisons of the performances of different statistical tests for time-to-event analysis with confounding factors: practical illustrations in kidney transplantation. Statistics in medicine. 30;35(7):1103-16, 2016. <doi:10.1002/ sim.6777>

Jun Xie and Chaofeng Liu. Adjusted Kaplan-Meier estimator and log-rank test with inverse probability of treatment weighting for survival data. Statistics in medicine, 24(20):3089-3110, 2005. <doi:10.1002/sim.2174>

Examples

data(dataDIVAT2)

# adjusted log-rank test
Pr0 <- glm(ecd ~ 1, family = binomial(link="logit"), data=dataDIVAT2)$fitted.values[1]
Pr1 <- glm(ecd ~ age + hla + retransplant, data=dataDIVAT2,
 family=binomial(link = "logit"))$fitted.values
W <- (dataDIVAT2$ecd==1) * (1/Pr1) + (dataDIVAT2$ecd==0) * (1)/(1-Pr1)

ipw.log.rank(dataDIVAT2$times, dataDIVAT2$failures, dataDIVAT2$ecd, W)

RISCA documentation built on March 31, 2023, 11:06 p.m.