ArticleBiostatistics (Oxford, England)2022
Causal inference for semi-competing risks data.
Article in Biostatistics (Oxford, England), 2022. 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
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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.
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Who cites it
16 citing papers in PubMed.
- Censoring, Competing Events, and Multistate Models: Comment on Beyersmann et al. "Hazards Constitute Key Quantities for Analyzing, Interpreting and Understanding Time-to-Event Data".Biometrical journal. Biometrische Zeitschrift · 2026Article
- Non-Parametric Mediation Analysis of Non-Markov Illness-Death Model.Statistics in medicine · 2026Article
- Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.Statistics in medicine · 2026Article
- An Overview and Recent Developments in the Analysis of Multistate Processes.Statistics in medicine · 2026Review
- Estimands and Doubly Robust Estimation for Cluster-Randomized Trials With Survival Outcomes.Statistics in medicine · 2026Article
- Assessing the Benefits and Burdens of Preventive Interventions.Statistics in medicine · 2026Article
- 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
- Survivor Average Causal Effects for Continuous Time: A Principal Stratification Approach to Causal Inference With Semicompeting Risks.Biometrical journal. Biometrische Zeitschrift · 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
- Article
- A BAYESIAN MACHINE LEARNING APPROACH FOR ESTIMATING HETEROGENEOUS SURVIVOR CAUSAL EFFECTS: APPLICATIONS TO A CRITICAL CARE TRIAL.The annals of applied statistics · 2024Article
- Selection Bias in Health Research: Quantifying, Eliminating, or Exacerbating Health Disparities?Current epidemiology reports · 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
- Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The causal effects of Apolipoprotein E $\epsilon4$ allele (APOE) on late-onset Alzheimer's disease (AD) and death are complicated to define because AD may occur under one intervention but not under the other, and because AD occurrence may affect age of death. In this article, this dual outcome scenario is studied using the semi-competing risks framework for time-to-event data. Two event times are of interest: a nonterminal event time (age at AD diagnosis), and a terminal event time (age at death). AD diagnosis time is observed only if it precedes death, which may occur before or after AD. We propose new estimands for capturing the causal effect of APOE on AD and death. Our proposal is based on a stratification of the population with respect to the order of the two events. We present a novel assumption utilizing the time-to-event nature of the data, which is more flexible than the often-invoked monotonicity assumption. We derive results on partial identifiability, suggest a sensitivity analysis approach, and give conditions under which full identification is possible. Finally, we present and implement nonparametric and semiparametric estimation methods under right-censored semi-competing risks data for studying the complex effect of APOE on AD and death.
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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.