ArticleAmerican journal of epidemiology2024
Identifying target populations to align with decision-makers' needs.
Article in American journal of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Extending Inferences from a Randomized Trial to Trial-eligible and Treatment-candidate Target Populations: Examples of Generalizability and Transportability.Epidemiology (Cambridge, Mass.) · 2026Article
- Moving From Representativeness Toward Transportability in an Era of Digital and Big Data.International journal of public health · 2026Review
- Revisiting representativeness.International journal of epidemiology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Randomized trials estimate the average treatment effect within individuals who are eligible, invited, and agree to enroll. However, decision-makers often require evidence that extends beyond the trial's enrolled population to inform policy or actions for their specific target population. Each decision-maker has distinct target populations, the composition of which may not often align with that of the trial population. As researchers, we should identify a decision-maker for whom we aim to generate evidence early in the research process. We can then specify a target population of their interest and determine if a policy or action can be informed using results from a trial alone, or if additional complementary real-world data and analysis are required. In this commentary, we outline 5 key groupings of decision-makers: policymakers, payers, purchasers, providers, and patients. We then specify relevant target populations for decision-makers interested in the effectiveness of beta-blockers after a myocardial infarction with preserved ejection fraction. Finally, we summarize the scenarios in which results from a randomized trial may or may not apply to these target populations and suggest relevant analytic approaches that can generate evidence to better align with a decision-maker's needs. This article is part of a Special Collection on Pharmacoepidemiology.
Indexed as
Identifiers
What OpenQuestion holds
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.