Survivor Average Causal Effects for Continuous Time: A Principal Stratification Approach to Causal Inference With Semicompeting Risks

Abstract

In semicompeting risks problems, nonterminal time-to-event outcomes, such as time to hospital readmission, are subject to truncation by death. These settings are often modeled with illness-death models for the hazards of the terminal and nonterminal events, but evaluating causal treatment effects with hazard models is problematic due to conditioning on survival-a posttreatment outcome-that is embedded in the definition of a hazard. Extending an existing survivor average causal effect (SACE) estimand, we frame the evaluation of treatment effects in the context of semicompeting risks with principal stratification and introduce two new causal estimands: the time-varying survivor average causal effect (TV-SACE) and the restricted mean survivor average causal effect (RM-SACE). These principal causal effects are defined among units that would survive regardless of assigned treatment. We adopt a Bayesian estimation procedure that parameterizes illness-death models for both treatment arms. We outline a frailty specification that can accommodate within-person correlation between nonterminal and terminal event times, and we discuss potential avenues for adding model flexibility. The method is demonstrated in the context of hospital readmission among late-stage pancreatic cancer patients.

Keywords: causal inference; hospital readmission; principal stratification; semicompeting risks; survivor average causal effect.

Figure 4

Reduction in covariate imbalance after propensity score matching of late-stage pancreatic cancer patients discharged to home care with support

Figure 1

Simulation Scenario 1: 95% credible intervals across 200 replicates for the fraction always-alive at $t$ (A), $TV-SACE(t,t)$ (B), and $RM-SACE(t,t)$ (C) for $t=30$, compared to the replicates’ finite sample truth (points) and the population average (vertical gray lines).

Figure 2

Posterior mean survival curves among newly diagnosed pancreatic cancer patients discharged home, with supportive care $(z=1)$ and without $(z=0)$, with the corresponding implications for always-alive principal stratum size (A) and posterior mean population composition of always-alive ($AA$), treatment-killed ($TK$), control-killed ($CK$), and doubly dead ($DD$) principal states (B)

Figure 3

Estimated time-varying ($TV-SACE$) and restricted mean ($RM-SACE$) survivor average causal effects of home care (vs. no additional care at home) on the cumulative incidence of hospital readmission among 6,280 newly diagnosed late-stage pancreatic cancer patients