ArticleJournal of causal inference2022
Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population.
Article in Journal of causal inference, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.Statistics in medicine · 2026Article
- COADVISE: covariate adjustment with variable selection in randomized controlled trials.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2026Article
- Reply to "Comparative Safety Profiles of Ocrelizumab and Rituximab in Multiple Sclerosis Treatment Using Real-World Evidence".Annals of neurology · 2026Article
- Data fusion methods for the heterogeneity of treatment effect and confounding function.Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability · 2025Article
- Statistical inference for heterogeneous treatment effect with right-censored data from synthesizing randomized clinical trials and real-world data.Biometrics · 2025Article
- Using overlap weights to address extreme propensity scores in estimating restricted mean counterfactual survival times.American journal of epidemiology · 2025Article
- Integrative analysis of high-dimensional RCT and RWD subject to censoring and hidden confounding.Lifetime data analysis · 2025Article
- Response to comment on "Transporting survival of an HIV clinical trial to the external target populations by Lee et al. (2024)".Journal of biopharmaceutical statistics · 2025Article
- Assessing racial disparities in healthcare expenditure using generalized propensity score weighting.BMC medical research methodology · 2025Article
- Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data.Journal of machine learning research : JMLR · 2025Article
- Article
- Estimating spatially varying health effects of wildland fire smoke using mobile health data.Journal of the Royal Statistical Society. Series C, Applied statistics · 2024Article
- When does adjusting covariate under randomization help? A comparative study on current practices.BMC medical research methodology · 2024Article
- genRCT: a statistical analysis framework for generalizing RCT findings to real-world population.Journal of biopharmaceutical statistics · 2024Article
- Causal Inference Methods for Combining Randomized Trials and Observational Studies: A Review.Statistical science : a review journal of the Institute of Mathematical Statistics · 2024Article
- Target Aggregate Data Adjustment Method for Transportability Analysis Utilizing Summary-Level Data From the Target Population.Pharmaceutical statisticsArticle
- Transporting randomized trial results to estimate counterfactual survival functions in target populations.Pharmaceutical statisticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
In the presence of heterogeneity between the randomized controlled trial (RCT) participants and the target population, evaluating the treatment effect solely based on the RCT often leads to biased quantification of the real-world treatment effect. To address the problem of lack of generalizability for the treatment effect estimated by the RCT sample, we leverage observational studies with large samples that are representative of the target population. This article concerns evaluating treatment effects on survival outcomes for a target population and considers a broad class of estimands that are functionals of treatment-specific survival functions, including differences in survival probability and restricted mean survival times. Motivated by two intuitive but distinct approaches, i.e., imputation based on survival outcome regression and weighting based on inverse probability of sampling, censoring, and treatment assignment, we propose a semiparametric estimator through the guidance of the efficient influence function. The proposed estimator is doubly robust in the sense that it is consistent for the target population estimands if either the survival model or the weighting model is correctly specified and is locally efficient when both are correct. In addition, as an alternative to parametric estimation, we employ the nonparametric method of sieves for flexible and robust estimation of the nuisance functions and show that the resulting estimator retains the root-
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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.