adjusted.LR: Log-Rank Test for Adjusted Survival Curves.

In IPWsurvival: Propensity Score Based Adjusted Survival Curves and Corresponding Log-Rank Statistic

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

adjusted.LR(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 weigths for correcting the contribution of each individual. By default, the weights are all equalled to 1 and the survival curves correspond to the usual Kaplan-Meier estimator.

Details

For instance, w 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	This parameter corresponds to the value of estimated log-rank statistic. If the weights are all equalled to 1, this value corresponds to the usual log-rank test.
p.value	The p-value associated to the previous log-rank statistic.

Author(s)

Y. Foucher <[e-mail]>,

J. Xie <[e-mail]> and

F. Le Borgne <[e-mail]>

References

Le Borgne F, Giraudeau B, Querard AH, Giral M and Foucher Y. 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(DIVAT)

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

adjusted.LR(DIVAT$times, DIVAT$failures, DIVAT$ecd, W)

IPWsurvival documentation built on May 1, 2019, 10:15 p.m.