Estimating Subgroup Effects Using the Propensity Score Method
A Practical Application in Outcomes Research
Bibliographic Data
| ID | 9103525 |
|---|---|
| Authors | Hester V Eeren (0000-0003-0624-9950, De Viersprong, corresponding author), Marieke D Spreeuwenberg (Maastricht University), Anna Bartak (University of Amsterdam), Mark de Rooij (0000-0001-7308-6210, Leiden University), Jan J V Busschbach (0000-0002-8602-0381, De Viersprong, corresponding author) |
| Year | 2015 |
| Volume | 53 |
| Issue | 4 |
| Pages | 366-373 |
| Publication date | 2015-04-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Medical Care (JOURNAL) |
| Journal identifiers | ISSN: 0025-7079 • E-ISSN: 1537-1948 |
| Publisher | Ovid Technologies (Wolters Kluwer Health) (PUBLISHER) |
| DOI | 10.1097/mlr.0000000000000325 |
| PMID | 25738381 |
| OpenAlex | W2319588407 |
| Language | EN |
| Citations received | 1 |
| References cited | 39 |
OBJECTIVE: Our aim was to demonstrate the feasibility of the univariate and generalized propensity score (PS) method in subgroup analysis of outcomes research. METHODS: First, to estimate subgroup effects, we tested the performance of 2 different PS methods, using Monte Carlo simulations: (1) the univariate PS with additional adjustment on the subgroup; and (2) the generalized PS, estimated by crossing the treatment options with a subgroup variable. The subgroup effects were estimated in a linear regression model using the 2 PS adjustments. We further explored whether the subgroup variable should be included in the univariate PS. Second, the 2 methods were compared using data from a large effectiveness study on psychotherapy in personality disorders. Using these data we tested the differences between short-term and long-term treatment, with the severity of patients' problems defining the subgroups of interest. RESULTS: The Monte Carlo simulations showed minor differences between both PS methods, with the bias and mean squared error overall marginally lower for the generalized PS. When considering the univariate PS, the subgroup variable can be excluded from the PS estimation and only adjusted for in the outcome equation. When applied to the psychotherapy data, the univariate and generalized PS estimations gave similar results. CONCLUSION: The results support the use of the generalized PS as a feasible method, compared with the univariate PS, to find certain subgroup effects in nonrandomized outcomes research
Confidence interval · Econometrics · Monte Carlo method · Multivariate analysis · Multivariate statistics · Propensity score matching · Statistics · Subgroup analysis · Univariate · Univariate analysis · Advanced Causal Inference Techniques · Mathematics · Medicine · Psychological Testing and Assessment · Psychometric Methodologies and Testing
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The role of the propensity score in estimating dose-response functions
Estimation of Causal Effects using Propensity Score Weighting
Combining Propensity Score Matching with Additional Adjustments for Prognostic Covariates
Variable Selection for Propensity Score Models
Estimating Causal Effects from Large Data Sets Using Propensity Scores
Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group
The central role of the propensity score in observational studies for causal effects
Estimating causal effects of treatments in randomized and nonrandomized studies.
Nonparametric Estimation of Average Treatment Effects Under Exogeneity
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies
The Multiple Propensity Score as Control for Bias in the Comparison of More Than Two Treatment Arms
| Citations per year | 0.25 |
|---|---|
| Citation span | 2022 - 2022 (1) |
| Highly cited | No |