Evidence map›Paper›PMID 41923006›Full record

ArticleBMC medical research methodology2026

The performance of different propensity score methods for estimating the effects of multiple treatments or exposures: a neutral comparison study.

Peter C Austin, David E Austin

Abstract readComparative Study
In one paragraph

Article in BMC medical research methodology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Peter C AustinICES, V106, 2075 Bayview Avenue, Toronto, Ontario, M4N 3M5, Canada. peter.austin@ices.on.ca.
David E AustinDepartment of Computer Science, McGill University, Montreal, Quebec, Canada.

Funding

CIHR PJT - 183902
6 · The paper itself

Abstract

backgroundThe generalized propensity score is an extension of the conventional propensity score to settings with a categorical exposure with more than two levels of treatment or exposure. Six different methods of using the generalized propensity score have been used in the general internal medical literature. However, no studies have evaluated the relative performance of these methods.

methodsWe used Monte Carlo simulations to evaluate the performance of seven methods for using the generalized propensity score to estimate the effect of three levels of exposure when outcomes are continuous or binary. We examined estimation of both the average treatment effect and the average treatment effect for the treated. These methods for using the generalized propensity score included: regression and weighting-based approaches proposed by Imbens, regression and weighting-based approaches proposed by McCaffrey, a regression-based approached proposed by Spreeuwenberg, Rubin’s pairwise comparison method, three-way matching, matching weights, and overlap weights. We illustrated the application of these methods by estimating the effect of smoking status (current smoker vs. former smoker vs. never smoker) on death within one year of hospitalization for acute myocardial infarction.

resultsNo method had consistently superior performance across all scenarios and target estimands.

conclusionWe make recommendations for the preferred method depending on the nature of the outcome and the target estimand.

Indexed as

Myocardial InfarctionOutcome Assessment, Health CarePropensity ScoreComputer SimulationHumansMonte Carlo MethodSmokingCausal inferenceGeneralized propensity scoreMonte Carlo simulationObservational studyPropensity score

Identifiers

PMID41923006
PMCPMC13169533

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Registered trials

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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.