ArticleJournal of biopharmaceutical statistics2024
genRCT: a statistical analysis framework for generalizing RCT findings to real-world population.
Article in Journal of biopharmaceutical statistics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 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
7 citing papers in PubMed.
- COADVISE: covariate adjustment with variable selection in randomized controlled trials.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2026Article
- Statistical inference for heterogeneous treatment effect with right-censored data from synthesizing randomized clinical trials and real-world data.Biometrics · 2025Article
- Enhancing randomized clinical trials with digital twins.NPJ systems biology and applications · 2025Review
- Optimal timing of cancer treatments: a call for emerging evidence from clinical trials and real-world studies.British journal of cancer · 2025Review
- 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
- Estimating spatially varying health effects of wildland fire smoke using mobile health data.Journal of the Royal Statistical Society. Series C, Applied statistics · 2024Article
- When does adjusting covariate under randomization help? A comparative study on current practices.BMC medical research methodology · 2024Article
Corrections and comments
- Erratum issuedCorrection.2024
Authors and funding
6 authors.
Funding
Abstract
When evaluating the real-world treatment effect, the analysis based on randomized clinical trials (RCTs) often introduces generalizability bias due to the difference in risk factors between the trial participants and the real-world patient population. This problem of lack of generalizability associated with the RCT-only analysis can be addressed by leveraging observational studies with large sample sizes that are representative of the real-world population. A set of novel statistical methods, termed "genRCT", for improving the generalizability of the trial has been developed using calibration weighting, which enforces the covariates balance between the RCT and observational study. This paper aims to review statistical methods for generalizing the RCT findings by harnessing information from large observational studies that represent real-world patients. Specifically, we discuss the choices of data sources and variables to meet key theoretical assumptions and principles. We introduce and compare estimation methods for continuous, binary, and survival endpoints. We showcase the use of the
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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.