ArticleEuropean journal of epidemiology2019
Effect heterogeneity and variable selection for standardizing causal effects to a target population.
Article in European journal of epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 1 citations in OpenAlex.
- Effect Modification and Its Impact on Preventable and Attributable Fractions in the Potential Outcomes Framework.Journal of epidemiology · 2026Article
- Bias due to non-consent in assisted reproductive treatment cohort studies: consent for disclosure to non-contact research in the Human Fertilisation and Embryology Authority register.Human reproduction (Oxford, England) · 2025Article
- When, why and how are estimated effects transported between populations? A scoping review of studies applying transportability methods.European journal of epidemiology · 2025Article
- Learning about treatment effects in a new target population under transportability assumptions for relative effect measures.European journal of epidemiology · 2024Article
- Selection Bias in Health Research: Quantifying, Eliminating, or Exacerbating Health Disparities?Current epidemiology reports · 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
- Transportability Analysis-A Tool for Extending Trial Results to a Representative Target Population.JAMA network open · 2024Article
- Review
- A directed acyclic graph for interactions.International journal of epidemiology · 2021Article
- Generalizing experimental results by leveraging knowledge of mechanisms.European journal of epidemiology · 2021Article
- Article
- Objectives, design and main findings until 2020 from the Rotterdam Study.European journal of epidemiology · 2020Article
- Generalizability of randomized trial results to target populations: Design and analysis possibilities.Research on social work practice · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 4 countries.
Funding
Abstract
The participants in randomized trials and other studies used for causal inference are often not representative of the populations seen by clinical decision-makers. To account for differences between populations, researchers may consider standardizing results to a target population. We discuss several different types of homogeneity conditions that are relevant for standardization: Homogeneity of effect measures, homogeneity of counterfactual outcome state transition parameters, and homogeneity of counterfactual distributions. Each of these conditions can be used to show that a particular standardization procedure will result in an unbiased estimate of the effect in the target population, given assumptions about the relevant scientific context. We compare and contrast the homogeneity conditions, in particular their implications for selection of covariates for standardization and their implications for how to compute the standardized causal effect in the target population. While some of the recently developed counterfactual approaches to generalizability rely upon homogeneity conditions that avoid many of the problems associated with traditional approaches, they often require adjustment for a large (and possibly unfeasible) set of covariates.
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.