ArticleAmerican journal of epidemiology2021
Study Designs for Extending Causal Inferences From a Randomized Trial to a Target Population.
Article in American journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 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.
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Who cites it
51 citing papers in PubMed.
- 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
- Special considerations in randomized trials investigating neonatal surgical treatments.Seminars in perinatology · 2022Trial
- Estimating the Effects of MTEER in U.S. Practice: A Transportability Analysis of the COAPT Trial.Journal of the American College of Cardiology · 2026Article
- Generalizing Conditional Average Treatment Effects From Nested Randomized Trials to Trial-Eligible Individuals.Statistics in medicine · 2026Article
- Extending Inferences from a Randomized Trial to Trial-eligible and Treatment-candidate Target Populations: Examples of Generalizability and Transportability.Epidemiology (Cambridge, Mass.) · 2026Article
- Long-term Cardiovascular Outcomes Following Bariatric Surgery: Reconciling Seemingly Conflicting Evidence.Epidemiology (Cambridge, Mass.) · 2026Article
- When does algorithmic complexity add causal value in environmental epidemiology?Environmental epidemiology (Philadelphia, Pa.) · 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
- Mapping the local effectiveness of mass drug administration for malaria using transportability methods.Nature health · 2026Article
- Modern Causal Inference Approaches to Improve Power for Subgroup Analysis in Randomized Controlled Trials.Statistics in medicine · 2026Article
- A biomarker-guided framework for corticosteroid treatment stratification in acute exacerbation of idiopathic pulmonary fibrosis.Frontiers in pharmacology · 2026Article
- Center-specific causal inference with multicenter trials-Interpreting trial evidence in the context of each participating center.Statistical methods in medical research · 2025Article
- Counterfactual prediction from machine learning models: transportability and joint analysis for model development and evaluation using multi-source data.Diagnostic and prognostic research · 2025Article
- The Target Study: A Conceptual Model and Framework for Measuring Disparity.Sociological methods & research · 2025Article
- Keep asking: What do I want? What do I have? What do I do? : An approach for combining information to learn about a target population.European journal of epidemiology · 2025Article
- Enough?Observational studies · 2025Article
- Transporting: What Is It and How Do You Do It?Current epidemiology reports · 2025Article
- Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data.Journal of machine learning research : JMLR · 2025Article
- Robust Estimation of Loss-Based Measures of Model Performance under Covariate Shift.The Canadian journal of statistics = Revue canadienne de statistique · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
In this article, we examine study designs for extending (generalizing or transporting) causal inferences from a randomized trial to a target population. Specifically, we consider nested trial designs, where randomized individuals are nested within a sample from the target population, and nonnested trial designs, including composite data-set designs, where observations from a randomized trial are combined with those from a separately obtained sample of nonrandomized individuals from the target population. We show that the counterfactual quantities that can be identified in each study design depend on what is known about the probability of sampling nonrandomized individuals. For each study design, we examine identification of counterfactual outcome means via the g-formula and inverse probability weighting. Last, we explore the implications of the sampling properties underlying the designs for the identification and estimation of the probability of trial participation.
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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.