ArticleBiometrics2019
Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals.
Article in Biometrics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 3 of them syntheses that pooled it.
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
86 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Reporting of Rurality, Deprivation, and Socioeconomic Characteristics in Cancer Clinical Trials: A Systematic Review.JAMA network open · 2026Pooled it
- Addressing Systematic Missing Data in the Context of Causally Interpretable Meta-analysis.Prevention science : the official journal of the Society for Prevention Research · 2023Pooled it
- Causally Interpretable Meta-analysis: Application in Adolescent HIV Prevention.Prevention science : the official journal of the Society for Prevention Research · 2022Pooled it
- Using methods to extend inferences to specific target populations to improve the precision of subgroup analyses.Journal of clinical epidemiology · 2025Trial
- Sensitivity analysis using bias functions for studies extending inferences from a randomized trial to a target population.Statistics in medicine · 2023Trial
- Regression-based estimation of heterogeneous treatment effects when extending inferences from a randomized trial to a target population.European journal of epidemiology · 2023Trial
- Extending Inferences from a Randomized Trial to Trial-eligible and Treatment-candidate Target Populations: Examples of Generalizability and Transportability.Epidemiology (Cambridge, Mass.) · 2026Article
- Clinical predictive artificial intelligence evaluation: A narrative review of trial designs and practical considerations.PLOS digital health · 2026Review
- Improving Sensitivity Analysis By Synthesizing Randomized Clinical Trials With Limited Overlap.Statistics in medicine · 2026Article
- Treatment effect estimation by comparing observed and predicted outcomes: conditions for valid inference and practical illustration.Diagnostic and prognostic research · 2026Article
- 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
- A causal inference framework for poststratification: a method for improving external validity in epidemiological studies.BMC medical research methodology · 2026Article
- Causal Inference With Survey Data: A Robust Framework for Propensity Score Weighting in Probability and Non-Probability Samples.Statistics in medicine · 2026Article
- A Primer on Large-Sample Statistical Inference for Epidemiologists.Epidemiologic methods · 2026Article
- Data Fusion for Partial Identification of Causal Effects.Advances in neural information processing systems · 2025Article
- Center-specific causal inference with multicenter trials-Interpreting trial evidence in the context of each participating center.Statistical methods in medical research · 2025Article
- Beyond reweighting: On the predictive role of covariate shift in effect generalization.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A Sensitivity Analysis Framework Using the Proxy Pattern-Mixture Model for Generalization of Experimental Results.Statistics in medicine · 2025Article
- 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
- Flexible and efficient estimation of causal effects with error-prone exposures: a control variates approach for measurement error.Biometrics · 2025Article
26 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
We consider methods for causal inference in randomized trials nested within cohorts of trial-eligible individuals, including those who are not randomized. We show how baseline covariate data from the entire cohort, and treatment and outcome data only from randomized individuals, can be used to identify potential (counterfactual) outcome means and average treatment effects in the target population of all eligible individuals. We review identifiability conditions, propose estimators, and assess the estimators' finite-sample performance in simulation studies. As an illustration, we apply the estimators in a trial nested within a cohort of trial-eligible individuals to compare coronary artery bypass grafting surgery plus medical therapy vs. medical therapy alone for chronic coronary artery disease.
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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.