Causal Risk Ratio and Causal Risk Difference in Longitudinal Studies With Frequent Outcome Events
Marginal structural models (MSMs) are recognized as useful methods for addressing the issue of time-varying confounding in longitudinal studies. In the analyses of longitudinal data with binary outcomes, using the generalized estimating equation (GEE) logistic regression model within the MSM framewo...
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| Format: | Article |
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Taylor & Francis Group
2025-12-01
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| Series: | Data Science in Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/26941899.2025.2527144 |
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| author | Hiroyuki Shiiba Hisashi Noma Keisuke Kuwahara Tohru Nakagawa Tetsuya Mizoue |
| author_facet | Hiroyuki Shiiba Hisashi Noma Keisuke Kuwahara Tohru Nakagawa Tetsuya Mizoue |
| author_sort | Hiroyuki Shiiba |
| collection | DOAJ |
| description | Marginal structural models (MSMs) are recognized as useful methods for addressing the issue of time-varying confounding in longitudinal studies. In the analyses of longitudinal data with binary outcomes, using the generalized estimating equation (GEE) logistic regression model within the MSM framework is a common approach to estimate the odds ratio. However, due to the interpretive issues with the odds ratio, recent statistical guidelines recommend the use of the risk ratio and the risk difference. Nevertheless, there are no applicable MSM methods using GEE that enable straightforward estimation for the causal risk ratio and risk difference. In this article, we provide two straightforward and effective methods for estimating the causal risk ratio and risk difference based on the MSM-GEE framework, using Poisson regression and normal linear regression models. We validated these methods through comprehensive simulation studies, confirming their unbiased estimation of the causal risk ratio and risk difference. Importantly, these methods remain effective even in situations where the MSM-GEE logistic regression model, which is the most widely used method for binary outcome data, yields biased estimates of the causal odds ratio. In addition, we applied the proposed methods to real-world longitudinal data and clearly demonstrated their practical effectiveness. |
| format | Article |
| id | doaj-art-14f468ec802648d8b37adb6acccf88d1 |
| institution | DOAJ |
| issn | 2694-1899 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Data Science in Science |
| spelling | doaj-art-14f468ec802648d8b37adb6acccf88d12025-08-20T02:43:15ZengTaylor & Francis GroupData Science in Science2694-18992025-12-014110.1080/26941899.2025.2527144Causal Risk Ratio and Causal Risk Difference in Longitudinal Studies With Frequent Outcome EventsHiroyuki Shiiba0Hisashi Noma1Keisuke Kuwahara2Tohru Nakagawa3Tetsuya Mizoue4The Graduate Institute for Advanced Studies, The Graduate University for Advanced Studies (SOKENDAI), Tokyo, JapanDepartment of Interdisciplinary Statistical Mathematics, The Institute of Statistical Mathematics, Tokyo, JapanDepartment of Epidemiology and Prevention, Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, JapanHitachi Health Care Center, Hitachi, Ltd, Hitachi, JapanDepartment of Epidemiology and Prevention, Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, JapanMarginal structural models (MSMs) are recognized as useful methods for addressing the issue of time-varying confounding in longitudinal studies. In the analyses of longitudinal data with binary outcomes, using the generalized estimating equation (GEE) logistic regression model within the MSM framework is a common approach to estimate the odds ratio. However, due to the interpretive issues with the odds ratio, recent statistical guidelines recommend the use of the risk ratio and the risk difference. Nevertheless, there are no applicable MSM methods using GEE that enable straightforward estimation for the causal risk ratio and risk difference. In this article, we provide two straightforward and effective methods for estimating the causal risk ratio and risk difference based on the MSM-GEE framework, using Poisson regression and normal linear regression models. We validated these methods through comprehensive simulation studies, confirming their unbiased estimation of the causal risk ratio and risk difference. Importantly, these methods remain effective even in situations where the MSM-GEE logistic regression model, which is the most widely used method for binary outcome data, yields biased estimates of the causal odds ratio. In addition, we applied the proposed methods to real-world longitudinal data and clearly demonstrated their practical effectiveness.https://www.tandfonline.com/doi/10.1080/26941899.2025.2527144Longitudinal studytime-varying confoundermarginal structural modelrisk ratiorisk difference |
| spellingShingle | Hiroyuki Shiiba Hisashi Noma Keisuke Kuwahara Tohru Nakagawa Tetsuya Mizoue Causal Risk Ratio and Causal Risk Difference in Longitudinal Studies With Frequent Outcome Events Data Science in Science Longitudinal study time-varying confounder marginal structural model risk ratio risk difference |
| title | Causal Risk Ratio and Causal Risk Difference in Longitudinal Studies With Frequent Outcome Events |
| title_full | Causal Risk Ratio and Causal Risk Difference in Longitudinal Studies With Frequent Outcome Events |
| title_fullStr | Causal Risk Ratio and Causal Risk Difference in Longitudinal Studies With Frequent Outcome Events |
| title_full_unstemmed | Causal Risk Ratio and Causal Risk Difference in Longitudinal Studies With Frequent Outcome Events |
| title_short | Causal Risk Ratio and Causal Risk Difference in Longitudinal Studies With Frequent Outcome Events |
| title_sort | causal risk ratio and causal risk difference in longitudinal studies with frequent outcome events |
| topic | Longitudinal study time-varying confounder marginal structural model risk ratio risk difference |
| url | https://www.tandfonline.com/doi/10.1080/26941899.2025.2527144 |
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