Fit the CIF using composite variable strategy for competing risks data, based on efficient influence functions

Source: R/surv_composite_eff.R

This function estimates the potential cumulative incidence function based on efficient influence functions using composite variable strategy (competing risks data structure). Cox models are employed for survival models. This strategy adopts the first occurrence of either the intermediate or primary event as the event of interest.

surv.composite.eff(A, Time, cstatus, X = NULL, subset = NULL)

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.

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 composite variable strategy addresses the problem of intercurrent events by expanding the outcome variables. It aggregates the intercurrent event and the primary outcome event into a single composite outcome variable. The idea is not new in the context of progression-free survival, where the composite outcome variable is defined as the occurrence of either a non-terminal event (e.g., cancer progression) or a terminal event (e.g., death). One widely used composite outcome variable has the form Q(w) = \min\{T(w), R(w)\} for w = 1, 0. When this simple form is adopted, the difference in counterfactual cumulative incidences is \tau(t) = P( Q(1) < t ) - P( Q(0) < t ), representing the difference in probabilities of experiencing either intercurrent events or primary outcome events during (0,t) under active treatment and placebo.