This function nonparametrically estimates the potential cumulative incidence function
using principal stratum strategy (competing risks data structure). The estimand is defined in a
subpopulation where intercurrent events would never occur regardless of treatment conditions.
surv.principal(A, Time, cstatus, weights = rep(1, length(A)), 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.
- weights
Weight for each subject.
- 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, which is not available under this strategy.
Details
The principal stratum strategy aims to stratify the population into subpopulations based on the joint
potential occurrences of intercurrent events under the two treatment assignments (R(1), R(0)).
Suppose we are interested in a principal stratum comprised of individuals who would never experience
intercurrent events, regardless of which treatment they receive. This principal stratum can be indicated
by \{R(1)=R(0)=\infty\}. The treatment effect is now defined within this subpopulation,
\tau(t) = P(T(1) < t \mid R(1)=R(0)=\infty) - P(T(0) < t \mid R(1)=R(0)=\infty),
representing the difference in probabilities of experiencing primary outcome events during (0,t)
under active treatment and placebo in the subpopulation that will not experience intercurrent events
regardless of treatment during (0,t). A principal ignorability assumption is made for identification.
If the size of the target principal stratum is small, the results could be highly variable.