Observational studyStatistics in medicine2022
A calibration approach to transportability and data-fusion with observational data.
Observational study in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 20 citations in OpenAlex.
- Fusing Specialized Surveys of Rare Populations to Larger Surveys for Generalized Inference: Cross-Sectional Survey Study.Journal of medical Internet research · 2026Article
- Introduction to secure data sharing in primary care using the federated causal learning models.BMJ health & care informatics · 2026Article
- Modern Causal Inference Approaches to Improve Power for Subgroup Analysis in Randomized Controlled Trials.Statistics in medicine · 2026Article
- Confidence Interval Construction for Causally Generalized Estimates With Target Sample Summary Information.Statistics in medicine · 2026Article
- Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects.Journal of the American Statistical Association · 2025Article
- Health Economic Evaluations of Obesity Interventions: Expert Views on How We Can Identify, Interpret, Analyse and Translate Effects.Applied health economics and health policy · 2025Article
- When, why and how are estimated effects transported between populations? A scoping review of studies applying transportability methods.European journal of epidemiology · 2025Article
- Use of transportability methods for real-world evidence generation: a review of current applications.Journal of comparative effectiveness research · 2024Review
- Generalizability of Randomized Clinical Trial Outcomes for Diabetes Control Resulting From Bariatric Surgery.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2024Article
- Transporting Comparative Effectiveness Evidence Between Countries: Considerations for Health Technology Assessments.PharmacoEconomics · 2024Article
- Transporting results in an observational epidemiology setting: purposes, methods, and applied example.Frontiers in epidemiology · 2024Article
- Recent Developments in Causal Inference and Machine Learning.Annual review of sociology · 2023Article
- Causal isotonic calibration for heterogeneous treatment effects.Proceedings of machine learning research · 2023Article
- Transporting observational study results to a target population of interest using inverse odds of participation weighting.PloS one · 2022Observational
- Transporting experimental results with entropy balancing.Statistics in medicine · 2021Article
- Target Aggregate Data Adjustment Method for Transportability Analysis Utilizing Summary-Level Data From the Target Population.Pharmaceutical statisticsArticle
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 3 institutions in 1 country.
Funding
Abstract
Two important considerations in clinical research studies are proper evaluations of internal and external validity. While randomized clinical trials can overcome several threats to internal validity, they may be prone to poor external validity. Conversely, large prospective observational studies sampled from a broadly generalizable population may be externally valid, yet susceptible to threats to internal validity, particularly confounding. Thus, methods that address confounding and enhance transportability of study results across populations are essential for internally and externally valid causal inference, respectively. These issues persist for another problem closely related to transportability known as data-fusion. We develop a calibration method to generate balancing weights that address confounding and sampling bias, thereby enabling valid estimation of the target population average treatment effect. We compare the calibration approach to two additional doubly robust methods that estimate the effect of an intervention on an outcome within a second, possibly unrelated target population. The proposed methodologies can be extended to resolve data-fusion problems that seek to evaluate the effects of an intervention using data from two related studies sampled from different populations. A simulation study is conducted to demonstrate the advantages and similarities of the different techniques. We also test the performance of the calibration approach in a motivating real data example comparing whether the effect of biguanides vs sulfonylureas-the two most common oral diabetes medication classes for initial treatment-on all-cause mortality described in a historical cohort applies to a contemporary cohort of US Veterans with diabetes.
Indexed as
Identifiers
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.