SynthesisBiostatistics (Oxford, England)2024
Systematically missing data in causally interpretable meta-analysis.
Synthesis in Biostatistics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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
8 citing papers in PubMed, 11 citations in OpenAlex.
- A causal meta-analysis framework for clinical trials with unequal randomization ratios.Research synthesis methods · 2026Article
- 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
- 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
- Network Meta-Analysis With Individual Participant-Level Data of Time-to-Event Outcomes Using Cox Regression.Statistics in medicine · 2025Article
- What works for whom in pediatric OCD: description of causally interpretable meta-analysis methods and report on trial data harmonization.Psychological medicine · 2025Review
- Estimating target population treatment effects in meta-analysis with individual participant-level data.Statistical methods in medical research · 2025Article
- Standardizing to specific target populations in distributed networks and multisite pharmacoepidemiologic studies.American journal of epidemiology · 2024Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Causally interpretable meta-analysis combines information from a collection of randomized controlled trials to estimate treatment effects in a target population in which experimentation may not be possible but from which covariate information can be obtained. In such analyses, a key practical challenge is the presence of systematically missing data when some trials have collected data on one or more baseline covariates, but other trials have not, such that the covariate information is missing for all participants in the latter. In this article, we provide identification results for potential (counterfactual) outcome means and average treatment effects in the target population when covariate data are systematically missing from some of the trials in the meta-analysis. We propose three estimators for the average treatment effect in the target population, examine their asymptotic properties, and show that they have good finite-sample performance in simulation studies. We use the estimators to analyze data from two large lung cancer screening trials and target population data from the National Health and Nutrition Examination Survey (NHANES). To accommodate the complex survey design of the NHANES, we modify the methods to incorporate survey sampling weights and allow for clustering.
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.