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DEFINING AND ESTIMATING PRINCIPAL STRATUM SPECIFIC NATURAL MEDIATION EFFECTS WITH SEMI-COMPETING RISKS DATA
Fei Gao1, Fan Xia2, K C G Chan3
1Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
Statistica Sinica
|April 10, 2026
Summary
This study introduces a new method to analyze medical data with intermediate and failure events, accounting for semi-competing risks. It defines direct and indirect effects for better understanding intervention impacts on patient outcomes.
Area of Science:
- Biostatistics
- Clinical Trials
- Epidemiology
Background:
- Medical studies often involve multiple related events, like intermediate clinical events and ultimate failure (e.g., death).
- Standard survival analysis methods struggle with semi-competing risks where one event can censor another, complicating effect estimation.
- Intermediate events can act as mediators, but conventional direct/indirect effect definitions are inadequate in semi-competing risks settings.
Purpose of the Study:
- To develop a framework for defining and estimating direct, indirect, and total effects in the presence of semi-competing risks.
- To address the limitations of conventional causal effect definitions in complex event time data structures.
- To propose a robust statistical methodology for analyzing interventions affecting both intermediate and failure events.
Main Methods:
- Defined three principal strata based on the potential occurrence of intermediate versus failure events.
- Proposed a semiparametric estimator utilizing a multivariate logistic stratum membership model.
- Employed within-stratum proportional hazards models for event time analysis.
- Developed an expectation-maximization algorithm to handle latent stratum membership.
Main Results:
- Successfully defined stratum-specific direct and indirect effects, and total effects applicable across strata.
- The proposed semiparametric estimator provides a method for identifying these effects under specified conditions.
- Numerical studies demonstrated the performance of the developed estimators.
Conclusions:
- The new statistical framework accurately defines and estimates causal effects in semi-competing risks data.
- This approach offers a valuable tool for researchers studying interventions with complex event structures in medical research.
- The proposed methods enhance the analysis of intermediate and failure events, improving understanding of treatment impacts.
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