Fit the CIF using while on treatment strategy for semicompeting risks data, based on efficient influence functions

Source: R/scr_whileon_eff.R

This function estimates the potential cumulative incidence function based on efficient influence functions using while on treatment strategy (semicompeting risks data structure). Cox models are employed for survival models. This strategy can be understood as the competing risks model, which gives the subdistribution of the primary event.

scr.whileon.eff(A, Time, status, Time_int, status_int, X = NULL, subset = NULL)

Arguments

A

Treatment indicator, 1 for treatment and 0 for control.

Time

Time to the primary (terminal) event.

status

Indicator of the primary (terminal) event, 1 for event and 0 for censoring.

Time_int

Time to the intercurrent event.

status_int

Indicator of the intercurrent event, 1 for event and 0 for censoring.

X

Baseline covariates.

subset

Subset, either numerical or logical.

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 the efficient influence function of the restricted mean survival time lost by the end of study.

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.