ArticleBiometrical journal. Biometrische Zeitschrift2025
Survivor Average Causal Effects for Continuous Time: A Principal Stratification Approach to Causal Inference With Semicompeting Risks.
Article in Biometrical journal. Biometrische Zeitschrift, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.Statistics in medicine · 2026Article
- Doubly Robust Estimators of the Restricted Mean Time in Favor Estimands in Individual- and Cluster-Randomized Trials.Statistics in medicine · 2026Article
- Causal Inference for First Non-Fatal Events With the Competing Risk of Death: A Principal Stratification Approach.Statistics in medicine · 2025Article
- Exploring Causal Effects of Hormone- and Radio-Treatments in an Observational Study of Breast Cancer Using Copula-Based Semi-Competing Risks Models.Statistics in medicine · 2025Article
- A Pseudo-Value Approach to Causal Deep Learning of Semi-Competing Risks.Arabian journal of mathematics · 2025Article
- Weighting methods for truncation by death in cluster-randomized trials.Statistical methods in medical research · 2025Article
- A BAYESIAN MACHINE LEARNING APPROACH FOR ESTIMATING HETEROGENEOUS SURVIVOR CAUSAL EFFECTS: APPLICATIONS TO A CRITICAL CARE TRIAL.The annals of applied statistics · 2024Article
- Surrogacy validation for time-to-event outcomes with illness-death frailty models.Biometrical journal. Biometrische Zeitschrift · 2024Article
- Estimation of separable direct and indirect effects in a continuous-time illness-death model.Lifetime data analysis · 2024Article
- causalCmprsk: An R package for nonparametric and Cox-based estimation of average treatment effects in competing risks data.Computer methods and programs in biomedicine · 2023Article
- DEFINING AND ESTIMATING PRINCIPAL STRATUM SPECIFIC NATURAL MEDIATION EFFECTS WITH SEMI-COMPETING RISKS DATA.Statistica Sinica · 2023Article
- Causal inference for semi-competing risks data.Biostatistics (Oxford, England) · 2022Article
- Optimising precision and power by machine learning in randomised trials with ordinal and time-to-event outcomes with an application to COVID-19.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2022Article
- A Bayesian nonparametric approach for evaluating the causal effect of treatment in randomized trials with semi-competing risks.Biostatistics (Oxford, England) · 2022Article
- Article
- Bayesian semi-parametric G-computation for causal inference in a cohort study with MNAR dropout and death.Journal of the Royal Statistical Society. Series C, Applied statistics · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.