surv.whileon: Fit CIFs using while on treatment strategy for competing risks data

Description

This function nonparametrically estimates the potential cumulative incidence function using while on treatment strategy (competing risks data structure). This strategy can be understood as the competing risks model, which gives the subdistribution of the primary event.

Usage

surv.whileon(A, Time, cstatus, weights = rep(1, length(A)))

Value

A list including

time1: Time points in the treated group.
time0: Time points in the control group.
cif1: Estimated cumulative incidence function in the treated group.
cif0: Estimated cumulative incidence function in the control group.
se1: Standard error of the estimated cumulative incidence function in the treated group.
se0: Standard error of the estimated cumulative incidence function in the control group.
time: Time points in both groups.
ate: Estimated treatment effect (difference in cumulative incidence functions).
se: Standard error of the estimated treatment effect.
p.val: P value of testing the treatment effect based on Gray test.

Arguments

A: Treatment indicator, 1 for treatment and 0 for control.
Time: Time to event.
cstatus: Indicator of event, 1 for the primary event, 2 for the intercurrent event, 0 for censoring.
weights: Weight for each subject.

Details

The while on treatment strategy considers the measure of outcome variables taken only up to the occurrence of intercurrent events. The failures of primary outcome events should not be counted in the cumulative incidences if intercurrent events occurred. The difference in counterfactual cumulative incidences under this strategy is \tau(t) = P(T(1) < t, R(1) \geq t) - P(T(0) < t, R(0) \geq t), representing the difference in probabilities of experiencing primary outcome events without intercurrent events during (0,t) under active treatment and placebo. The cumulative incidence function is also known as the cause-specific cumulative incidence or subdistribution function. The while on treatment strategy is closely related to the competing risks model. However, for causal interpretations, it is worth emphasizing that the hazard of R(1) may differ from that of R(0), leading to vast difference in the underlying features of individuals who have not experienced the primary outcome event between treatment conditions at any time t \in (0,t^*), where t^* is the end of study. When the scientific question of interest is the impact of treatment on the primary outcome event, the estimand \tau(t) is hard to interpret if a systematic difference in the risks of intercurrent events between two treatment conditions under comparison is anticipated.

Description

Usage

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Details

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