ArticleStatistics in medicine2026
Confidence Interval Construction for Causally Generalized Estimates With Target Sample Summary Information.
Article in Statistics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Generalizing causal findings, such as the average treatment effect (ATE), from a source to a target population is a critical topic in biomedical research. Differences in the distributions of treatment effect modifiers between these populations, known as covariate shift, can lead to varying ATEs. Chen et al. [1] introduced a weighting method to estimate the target ATE using only summary-level information from a target sample while accounting for the possible covariate shifts. However, the asymptotic variance of the estimate was shown to depend on individual-level data from the target sample, hindering statistical inference. In this article, we propose a resampling-based perturbation method for confidence interval construction for the estimated target ATE, utilizing additional summary-level information. We demonstrate the effectiveness of our approach through simulation and real data settings when only summary-level information is available.
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What Socratic 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 Socratic graph.