ArticlePloS one2018
Sensitivity analyses for effect modifiers not observed in the target population when generalizing treatment effects from a randomized controlled trial: Assumptions, models, effect scales, data scenarios, and implementation details.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 34 citations in OpenAlex.
- Sensitivity analysis using bias functions for studies extending inferences from a randomized trial to a target population.Statistics in medicine · 2023Trial
- Generalizing randomized trial findings to a target population using complex survey population data.Statistics in medicine · 2021Trial
- Transporting Results from a Trial to an External Target Population When Trial Participation Impacts Adherence.Epidemiology (Cambridge, Mass.) · 2026Article
- Data Fusion for Partial Identification of Causal Effects.Advances in neural information processing systems · 2025Article
- A Sensitivity Analysis Framework Using the Proxy Pattern-Mixture Model for Generalization of Experimental Results.Statistics in medicine · 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
- 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
- Transporting: What Is It and How Do You Do It?Current epidemiology reports · 2025Article
- Synthesis estimators for transportability with positivity violations by a continuous covariate.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2025Article
- Sensitivity analysis for studies transporting prediction models.Biometrics · 2024Article
- Transporting Comparative Effectiveness Evidence Between Countries: Considerations for Health Technology Assessments.PharmacoEconomics · 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
- Generalizability and Transportability of the National Lung Screening Trial Data: Extending Trial Results to Different Populations.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2021Article
- Applicability of Transcatheter Aortic Valve Replacement Trials to Real-World Clinical Practice: Findings From EXTEND-CoreValve.JACC. Cardiovascular interventions · 2021Article
- Transportability From Randomized Trials to Clinical Care: On Initial HIV Treatment With Efavirenz and Suicidal Thoughts or Behaviors.American journal of epidemiology · 2021Article
- Transporting experimental results with entropy balancing.Statistics in medicine · 2021Article
- Recruitment Challenges for Low-Risk Health System Intervention Trials in Older Adults: A Case Study.Journal of the American Geriatrics Society · 2020Article
- Target validity: Bringing treatment of external validity in line with internal validity.Current epidemiology reports · 2020Article
- Extending inferences from a randomized trial to a target population.European journal of epidemiology · 2019Article
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 at 2 institutions in 1 country.
Funding
Abstract
backgroundRandomized controlled trials are often used to inform policy and practice for broad populations. The average treatment effect (ATE) for a target population, however, may be different from the ATE observed in a trial if there are effect modifiers whose distribution in the target population is different that from that in the trial. Methods exist to use trial data to estimate the target population ATE, provided the distributions of treatment effect modifiers are observed in both the trial and target population-an assumption that may not hold in practice.
methodsThe proposed sensitivity analyses address the situation where a treatment effect modifier is observed in the trial but not the target population. These methods are based on an outcome model or the combination of such a model and weighting adjustment for observed differences between the trial sample and target population. They accommodate several types of outcome models: linear models (including single time outcome and pre- and post-treatment outcomes) for additive effects, and models with log or logit link for multiplicative effects. We clarify the methods' assumptions and provide detailed implementation instructions. ILLUSTRATION: We illustrate the methods using an example generalizing the effects of an HIV treatment regimen from a randomized trial to a relevant target population.
conclusionThese methods allow researchers and decision-makers to have more appropriate confidence when drawing conclusions about target population effects.
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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.