ArticleStatistical science : a review journal of the Institute of Mathematical Statistics2024
Causal Inference Methods for Combining Randomized Trials and Observational Studies: A Review.
Article in Statistical science : a review journal of the Institute of Mathematical Statistics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 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
53 citing papers in PubMed.
- Transportable inference using target population summary statistics under covariate shift.Biometrics · 2026Article
- Considerations for the Integration of Clinical Trials and Real-World Data.Clinical pharmacology and therapeutics · 2026Review
- Generalizing Conditional Average Treatment Effects From Nested Randomized Trials to Trial-Eligible Individuals.Statistics in medicine · 2026Article
- Scoping review of methodology for aiding generalisability and transportability of clinical prediction models.Diagnostic and prognostic research · 2026Review
- Representation Retrieval Learning for Heterogeneous Data Integration.Journal of the American Statistical Association · 2026Article
- Clinical predictive artificial intelligence evaluation: A narrative review of trial designs and practical considerations.PLOS digital health · 2026Review
- Extrapolating Vincristine-Induced Peripheral Neuropathy From Caucasian to Kenyan Populations: Impact of Type I and Type II Selection Bias.CPT: pharmacometrics & systems pharmacology · 2026Article
- Heterogeneous wealth-related differences in modern contraceptive use in Bangladesh: a machine learning-based g-computation analysis of the DHS 2022 cross-sectional survey.Journal of health, population, and nutrition · 2026Observational
- The association of Hashimoto's thyroiditis with central lymph node metastasis in papillary thyroid cancer: a retrospective cross-sectional study.Gland surgery · 2026Article
- Automated candidate confounder scoping for adjustment in clinical research: a retrieval-augmented generation approach.BioData mining · 2026Article
- Innovative Clinical Trial Approach for Evaluating Digital Medical Devices Under European Fast-Track Regulatory Frameworks.Statistics in medicine · 2026Article
- A novel average treatment effect estimation approach for integrated incomplete datasets, with application in childhood anemia.BMC medical research methodology · 2026Article
- Shortening the Stay for Youths in Acute Care Hospitals: A Scoping Review of Interventions.Psychiatric services (Washington, D.C.) · 2026Article
- Modern Causal Inference Approaches to Improve Power for Subgroup Analysis in Randomized Controlled Trials.Statistics in medicine · 2026Article
- Distributed fusion R-learner of heterogeneous treatment effect using distributed medicaid data.Biometrics · 2026Article
- Confidence Interval Construction for Causally Generalized Estimates With Target Sample Summary Information.Statistics in medicine · 2026Article
- Safely managed drinking water service use and child diarrhoea based on evidence from 24 countries.Nature water · 2026Article
- Precision Mental Health: Predicting Heterogeneous Treatment Effects for Depression through Data Integration.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- Data Fusion for Partial Identification of Causal Effects.Advances in neural information processing systems · 2025Article
- Beyond reweighting: On the predictive role of covariate shift in effect generalization.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
With increasing data availability, causal effects can be evaluated across different data sets, both randomized controlled trials (RCTs) and observational studies. RCTs isolate the effect of the treatment from that of unwanted (confounding) co-occurring effects but they may suffer from unrepresentativeness, and thus lack external validity. On the other hand, large observational samples are often more representative of the target population but can conflate confounding effects with the treatment of interest. In this paper, we review the growing literature on methods for causal inference on combined RCTs and observational studies, striving for the best of both worlds. We first discuss identification and estimation methods that improve generalizability of RCTs using the representativeness of observational data. Classical estimators include weighting, difference between conditional outcome models and doubly robust estimators. We then discuss methods that combine RCTs and observational data to either ensure unconfoundedness of the observational analysis or to improve (conditional) average treatment effect estimation. We also connect and contrast works developed in both the potential outcomes literature and the structural causal model literature. Finally, we compare the main methods using a simulation study and real world data to analyze the effect of tranexamic acid on the mortality rate in major trauma patients. A review of available codes and new implementations is also provided.
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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.