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Covariate balance-related propensity score weighting in estimating overall hazard ratio with distributed survival data
BACKGROUND: When data is distributed across multiple sites, sharing information at the individual level among sites may be difficult. In these multi-site studies, propensity score model can be fitted with data within each site or data from all sites when using inverse probability-weighted Cox regres...
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/PMC10576397/ https://www.ncbi.nlm.nih.gov/pubmed/37833641 http://dx.doi.org/10.1186/s12874-023-02055-8 |
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author | Huang, Chen Wei, Kecheng Wang, Ce Yu, Yongfu Qin, Guoyou |
author_facet | Huang, Chen Wei, Kecheng Wang, Ce Yu, Yongfu Qin, Guoyou |
author_sort | Huang, Chen |
collection | PubMed |
description | BACKGROUND: When data is distributed across multiple sites, sharing information at the individual level among sites may be difficult. In these multi-site studies, propensity score model can be fitted with data within each site or data from all sites when using inverse probability-weighted Cox regression to estimate overall hazard ratio. However, when there is unknown heterogeneity of covariates in different sites, either approach may lead to potential bias or reduced efficiency. In this study, we proposed a method to estimate propensity score based on covariate balance-related criterion and estimate the overall hazard ratio while overcoming data sharing constraints across sites. METHODS: The proposed propensity score was generated by choosing between global and local propensity score based on covariate balance-related criterion, combining the global propensity score fitted in the entire population and the local propensity score fitted within each site. We used this proposed propensity score to estimate overall hazard ratio of distributed survival data with multiple sites, while requiring only the summary-level information across sites. We conducted simulation studies to evaluate the performance of the proposed method. Besides, we applied the proposed method to real-world data to examine the effect of radiation therapy on time to death among breast cancer patients. RESULTS: The simulation studies showed that the proposed method improved the performance in estimating overall hazard ratio comparing with global and local propensity score method, regardless of the number of sites and sample size in each site. Similar results were observed under both homogeneous and heterogeneous settings. Besides, the proposed method yielded identical results to the pooled individual-level data analysis. The real-world data analysis indicated that the proposed method was more likely to find a significant effect of radiation therapy on mortality compared to the global propensity score method and local propensity score method. CONCLUSIONS: The proposed covariate balance-related propensity score in multi-site distributed survival data outperformed the global propensity score estimated using data from the entire population or the local propensity score estimated within each site in estimating the overall hazard ratio. The proposed approach can be performed without individual-level data transfer between sites and would yield the same results as the corresponding pooled individual-level data analysis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-02055-8. |
format | Online Article Text |
id | pubmed-10576397 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105763972023-10-15 Covariate balance-related propensity score weighting in estimating overall hazard ratio with distributed survival data Huang, Chen Wei, Kecheng Wang, Ce Yu, Yongfu Qin, Guoyou BMC Med Res Methodol Research BACKGROUND: When data is distributed across multiple sites, sharing information at the individual level among sites may be difficult. In these multi-site studies, propensity score model can be fitted with data within each site or data from all sites when using inverse probability-weighted Cox regression to estimate overall hazard ratio. However, when there is unknown heterogeneity of covariates in different sites, either approach may lead to potential bias or reduced efficiency. In this study, we proposed a method to estimate propensity score based on covariate balance-related criterion and estimate the overall hazard ratio while overcoming data sharing constraints across sites. METHODS: The proposed propensity score was generated by choosing between global and local propensity score based on covariate balance-related criterion, combining the global propensity score fitted in the entire population and the local propensity score fitted within each site. We used this proposed propensity score to estimate overall hazard ratio of distributed survival data with multiple sites, while requiring only the summary-level information across sites. We conducted simulation studies to evaluate the performance of the proposed method. Besides, we applied the proposed method to real-world data to examine the effect of radiation therapy on time to death among breast cancer patients. RESULTS: The simulation studies showed that the proposed method improved the performance in estimating overall hazard ratio comparing with global and local propensity score method, regardless of the number of sites and sample size in each site. Similar results were observed under both homogeneous and heterogeneous settings. Besides, the proposed method yielded identical results to the pooled individual-level data analysis. The real-world data analysis indicated that the proposed method was more likely to find a significant effect of radiation therapy on mortality compared to the global propensity score method and local propensity score method. CONCLUSIONS: The proposed covariate balance-related propensity score in multi-site distributed survival data outperformed the global propensity score estimated using data from the entire population or the local propensity score estimated within each site in estimating the overall hazard ratio. The proposed approach can be performed without individual-level data transfer between sites and would yield the same results as the corresponding pooled individual-level data analysis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-02055-8. BioMed Central 2023-10-13 /pmc/articles/PMC10576397/ /pubmed/37833641 http://dx.doi.org/10.1186/s12874-023-02055-8 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 Huang, Chen Wei, Kecheng Wang, Ce Yu, Yongfu Qin, Guoyou Covariate balance-related propensity score weighting in estimating overall hazard ratio with distributed survival data |
title | Covariate balance-related propensity score weighting in estimating overall hazard ratio with distributed survival data |
title_full | Covariate balance-related propensity score weighting in estimating overall hazard ratio with distributed survival data |
title_fullStr | Covariate balance-related propensity score weighting in estimating overall hazard ratio with distributed survival data |
title_full_unstemmed | Covariate balance-related propensity score weighting in estimating overall hazard ratio with distributed survival data |
title_short | Covariate balance-related propensity score weighting in estimating overall hazard ratio with distributed survival data |
title_sort | covariate balance-related propensity score weighting in estimating overall hazard ratio with distributed survival data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10576397/ https://www.ncbi.nlm.nih.gov/pubmed/37833641 http://dx.doi.org/10.1186/s12874-023-02055-8 |
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