ArticleBiometrics2023
Efficient and robust methods for causally interpretable meta-analysis: Transporting inferences from multiple randomized trials to a target population.
Article in Biometrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
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.
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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
36 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Systematically missing data in causally interpretable meta-analysis.Biostatistics (Oxford, England) · 2024Pooled it
- Transportability Methods for Time-to-Event Outcomes: Application in Adjuvant Colon Cancer Trials.JCO clinical cancer informatics · 2022Pooled 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
- Transportable inference using target population summary statistics under covariate shift.Biometrics · 2026Article
- Generalizing Conditional Average Treatment Effects From Nested Randomized Trials to Trial-Eligible Individuals.Statistics in medicine · 2026Article
- Integrating Meta-Analysis Into a Specific Study (InMASS) for Estimating the Target-Population Average Treatment Effect.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Transportability Analyses in Comparative Effectiveness Research: A Conceptual Framework and Methodological Principles Endorsed by the International Society for Pharmacoepidemiology.Pharmacoepidemiology and drug safety · 2026Article
- Evaluating Cardiovascular Devices Using Observational Analyses.Circulation · 2026Review
- Innovative Clinical Trial Approach for Evaluating Digital Medical Devices Under European Fast-Track Regulatory Frameworks.Statistics in medicine · 2026Article
- A causal meta-analysis framework for clinical trials with unequal randomization ratios.Research synthesis methods · 2026Article
- Some Common Dose-Exposure-Response Estimands and Conditions for Their Causal Identifiability.CPT: pharmacometrics & systems pharmacology · 2026Article
- A surrogate endpoint-based provisional approval causal roadmap, illustrated by vaccine development.Biostatistics (Oxford, England) · 2025Article
- Multi-study R-learner for estimating heterogeneous treatment effects across studies using statistical machine learning.Biostatistics (Oxford, England) · 2025Article
- A Review of Methods for Research Synthesis.Statistics in medicine · 2025Article
- Center-specific causal inference with multicenter trials-Interpreting trial evidence in the context of each participating center.Statistical methods in medical research · 2025Article
- Immune Correlates and Vaccine Immunobridging: Statistical Innovations, Challenges, and Opportunities.The Journal of infectious diseases · 2025Review
- Causal inference in multi-cohort studies using the target trial framework to identify and minimize sources of bias.American journal of epidemiology · 2025Article
- A Two-Stage Method for Extending Inferences From a Collection of Trials.Statistics in medicine · 2025Article
- Data fusion using weakly aligned sources.Journal of the American Statistical Association · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
We present methods for causally interpretable meta-analyses that combine information from multiple randomized trials to draw causal inferences for a target population of substantive interest. We consider identifiability conditions, derive implications of the conditions for the law of the observed data, and obtain identification results for transporting causal inferences from a collection of independent randomized trials to a new target population in which experimental data may not be available. We propose an estimator for the potential outcome mean in the target population under each treatment studied in the trials. The estimator uses covariate, treatment, and outcome data from the collection of trials, but only covariate data from the target population sample. We show that it is doubly robust in the sense that it is consistent and asymptotically normal when at least one of the models it relies on is correctly specified. We study the finite sample properties of the estimator in simulation studies and demonstrate its implementation using data from a multicenter randomized trial.
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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.