Fit CIFs using hypothetical strategy (II) for semicompeting risks data, based on efficient influence functions

This function estimates the potential cumulative incidence function based on efficient influence functions using hypothetical strategy (semicompeting risks data structure). Cox models are employed for survival models. The intercurrent event is assumed to be absent in the hypothetical scenario.

Usage

scr.removed.eff(A, Time, status, Time_int, status_int, X = 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.

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 hypothetical strategy envisions a hypothetical clinical trial condition where the occurrence of intercurrent events is restricted in certain ways. By doing so, the distribution of potential outcomes under the hypothetical scenario can capture the impact of intercurrent events explicitly through a pre-specified criterion. We use T'(w), w = 1, 0 to denote the time to the primary outcome event in the hypothetical scenario. The time-dependent treatment effect specific to this hypothetical scenario is written as \tau(t) = P(T'(1) < t) - P(T'(0) < t), representing the difference in probabilities of experiencing primary outcome events during (0,t) in the pre-specified hypothetical scenario under active treatment and placebo. The key question is how to envision T'(w). We manipulate the hazard specific to intercurrent event \lambda_2(t; w) while assuming the cause-specific hazard specific to the primary outcome event under no intercurrent events \lambda_1(t; w) remains unchanged. Specifically, we envision that intercurrent events are absent in the hypothetical scenario for all individuals, so \lambda_2'(t;0) = \Lambda_2'(t;1) = 0. This hypothetical scenario leads to an estimand called the marginal cumulative incidence. The treatment effect corresponds to the controlled direct effect with the intercurrent events removed.

See also

scr.removed, scr.tteICE