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How to perform prespecified subgroup analyses when using propensity score methods in the case of imbalanced subgroups
BACKGROUND: Looking for treatment-by-subset interaction on a right-censored outcome based on observational data using propensity-score (PS) modeling is of interest. However, there are still issues regarding its implementation, notably when the subsets are very imbalanced in terms of prognostic featu...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10617117/ https://www.ncbi.nlm.nih.gov/pubmed/37907863 http://dx.doi.org/10.1186/s12874-023-02071-8 |
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author | Chatelet, Florian Verillaud, Benjamin Chevret, Sylvie |
author_facet | Chatelet, Florian Verillaud, Benjamin Chevret, Sylvie |
author_sort | Chatelet, Florian |
collection | PubMed |
description | BACKGROUND: Looking for treatment-by-subset interaction on a right-censored outcome based on observational data using propensity-score (PS) modeling is of interest. However, there are still issues regarding its implementation, notably when the subsets are very imbalanced in terms of prognostic features and treatment prevalence. METHODS: We conducted a simulation study to compare two main PS estimation strategies, performed either once on the whole sample (“across subset”) or in each subset separately (“within subsets”). Several PS models and estimands are also investigated. We then illustrated those approaches on the motivating example, namely, evaluating the benefits of facial nerve resection in patients with parotid cancer in contact with the nerve, according to pretreatment facial palsy. RESULTS: Our simulation study demonstrated that both strategies provide close results in terms of bias and variance of the estimated treatment effect, with a slight advantage for the “across subsets” strategy in very small samples, provided that interaction terms between the subset variable and other covariates influencing the choice of treatment are incorporated. PS matching without replacement resulted in biased estimates and should be avoided in the case of very imbalanced subsets. CONCLUSIONS: When assessing heterogeneity in the treatment effect in small samples, the “across subsets” strategy of PS estimation is preferred. Then, either a PS matching with replacement or a weighting method must be used to estimate the average treatment effect in the treated or in the overlap population. In contrast, PS matching without replacement should be avoided in this setting. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-02071-8. |
format | Online Article Text |
id | pubmed-10617117 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106171172023-11-01 How to perform prespecified subgroup analyses when using propensity score methods in the case of imbalanced subgroups Chatelet, Florian Verillaud, Benjamin Chevret, Sylvie BMC Med Res Methodol Research BACKGROUND: Looking for treatment-by-subset interaction on a right-censored outcome based on observational data using propensity-score (PS) modeling is of interest. However, there are still issues regarding its implementation, notably when the subsets are very imbalanced in terms of prognostic features and treatment prevalence. METHODS: We conducted a simulation study to compare two main PS estimation strategies, performed either once on the whole sample (“across subset”) or in each subset separately (“within subsets”). Several PS models and estimands are also investigated. We then illustrated those approaches on the motivating example, namely, evaluating the benefits of facial nerve resection in patients with parotid cancer in contact with the nerve, according to pretreatment facial palsy. RESULTS: Our simulation study demonstrated that both strategies provide close results in terms of bias and variance of the estimated treatment effect, with a slight advantage for the “across subsets” strategy in very small samples, provided that interaction terms between the subset variable and other covariates influencing the choice of treatment are incorporated. PS matching without replacement resulted in biased estimates and should be avoided in the case of very imbalanced subsets. CONCLUSIONS: When assessing heterogeneity in the treatment effect in small samples, the “across subsets” strategy of PS estimation is preferred. Then, either a PS matching with replacement or a weighting method must be used to estimate the average treatment effect in the treated or in the overlap population. In contrast, PS matching without replacement should be avoided in this setting. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-02071-8. BioMed Central 2023-10-31 /pmc/articles/PMC10617117/ /pubmed/37907863 http://dx.doi.org/10.1186/s12874-023-02071-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Chatelet, Florian Verillaud, Benjamin Chevret, Sylvie How to perform prespecified subgroup analyses when using propensity score methods in the case of imbalanced subgroups |
title | How to perform prespecified subgroup analyses when using propensity score methods in the case of imbalanced subgroups |
title_full | How to perform prespecified subgroup analyses when using propensity score methods in the case of imbalanced subgroups |
title_fullStr | How to perform prespecified subgroup analyses when using propensity score methods in the case of imbalanced subgroups |
title_full_unstemmed | How to perform prespecified subgroup analyses when using propensity score methods in the case of imbalanced subgroups |
title_short | How to perform prespecified subgroup analyses when using propensity score methods in the case of imbalanced subgroups |
title_sort | how to perform prespecified subgroup analyses when using propensity score methods in the case of imbalanced subgroups |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10617117/ https://www.ncbi.nlm.nih.gov/pubmed/37907863 http://dx.doi.org/10.1186/s12874-023-02071-8 |
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