Trial reportPharmacoepidemiology and drug safety2013
Investigating differences in treatment effect estimates between propensity score matching and weighting: a demonstration using STAR*D trial data.
Trial report in Pharmacoepidemiology and drug safety, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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, 1 synthesis or guideline pooled it.
- Performance of propensity score methods in observational studies: a systematic review.Systematic reviews · 2026Pooled it
- Inpatient COVID-19 outcomes in solid organ transplant recipients compared to non-solid organ transplant patients: A retrospective cohort.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2021Article
- Vector-based kernel weighting: A simple estimator for improving precision and bias of average treatment effects in multiple treatment settings.Statistics in medicine · 2021Article
- Single-arm Trials With External Comparators and Confounder Misclassification: How Adjustment Can Fail.Medical care · 2020Article
- Propensity score methods to control for confounding in observational cohort studies: a statistical primer and application to endoscopy research.Gastrointestinal endoscopy · 2019Review
- Propensity scores for confounder adjustment when assessing the effects of medical interventions using nonexperimental study designs.Journal of internal medicine · 2014Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
purposeThe choice of propensity score (PS) implementation influences treatment effect estimates not only because different methods estimate different quantities, but also because different estimators respond in different ways to phenomena such as treatment effect heterogeneity and limited availability of potential matches. Using effectiveness data, we describe lessons learned from sensitivity analyses with matched and weighted estimates.
methodsWith subsample data (N = 1292) from Sequenced Treatment Alternatives to Relieve Depression, a 2001-2004 effectiveness trial of depression treatments, we implemented PS matching and weighting to estimate the treatment effect in the treated and conducted multiple sensitivity analyses.
resultsMatching and weighting both balanced covariates but yielded different samples and treatment effect estimates (matched RR 1.00, 95% CI: 0.75-1.34; weighted RR 1.28, 95% CI: 0.97-1.69). In sensitivity analyses, as increasing numbers of observations at both ends of the PS distribution were excluded from the weighted analysis, weighted estimates approached the matched estimate (weighted RR 1.04, 95% CI 0.77-1.39 after excluding all observations below the 5th percentile of the treated and above the 95th percentile of the untreated). Treatment appeared to have benefits only in the highest and lowest PS strata.
conclusionsMatched and weighted estimates differed due to incomplete matching, sensitivity of weighted estimates to extreme observations, and possibly treatment effect heterogeneity. PS analysis requires identifying the population and treatment effect of interest, selecting an appropriate implementation method, and conducting and reporting sensitivity analyses. Weighted estimation especially should include sensitivity analyses relating to influential observations, such as those treated contrary to prediction.
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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.