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Xu J, He Q, Wang M, Liu M, Li Q, Ren Y, Yao M, Li G, Lu K, Zou K, Wang W, Sun X. Handling time-varying treatments in observational studies: A scoping review and recommendations. J Evid Based Med 2024; 17:95-105. [PMID: 38502877 DOI: 10.1111/jebm.12600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 03/05/2024] [Indexed: 03/21/2024]
Abstract
OBJECTIVE Time-varying treatments are common in observational studies. However, when assessing treatment effects, the methodological framework has not been systematically established for handling time-varying treatments. This study aimed to examine the current methods for dealing with time-varying treatments in observational studies and developed practical recommendations. METHODS We searched PubMed from 2000 to 2021 for methodological articles about time-varying treatments, and qualitatively summarized the current methods for handling time-varying treatments. Subsequently, we developed practical recommendations through interactive internal group discussions and consensus by a panel of external experts. RESULTS Of the 36 eligible reports (22 methodological reviews, 10 original studies, 2 tutorials and 2 commentaries), most examined statistical methods for time-varying treatments, and only a few discussed the overarching methodological process. Generally, there were three methodological components to handle time-varying treatments. These included the specification of treatment which may be categorized as three scenarios (i.e., time-independent treatment, static treatment regime, or dynamic treatment regime); definition of treatment status which could involve three approaches (i.e., intention-to-treat, per-protocol, or as-treated approach); and selection of analytic methods. Based on the review results, a methodological workflow and a set of practical recommendations were proposed through two consensus meetings. CONCLUSIONS There is no consensus process for assessing treatment effects in observational studies with time-varying treatments. Previous efforts were dedicated to developing statistical methods. Our study proposed a stepwise workflow with practical recommendations to assist the practice.
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Affiliation(s)
- Jiayue Xu
- Chinese Evidence-Based Medicine and Cochrane China Center, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu, China
- National Medical Products Administration Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real World Data, Chengdu, China
| | - Qiao He
- Chinese Evidence-Based Medicine and Cochrane China Center, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu, China
- National Medical Products Administration Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real World Data, Chengdu, China
| | - Mingqi Wang
- Chinese Evidence-Based Medicine and Cochrane China Center, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu, China
- National Medical Products Administration Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real World Data, Chengdu, China
| | - Mei Liu
- Chinese Evidence-Based Medicine and Cochrane China Center, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu, China
- National Medical Products Administration Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real World Data, Chengdu, China
| | - Qianrui Li
- Chinese Evidence-Based Medicine and Cochrane China Center, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu, China
- National Medical Products Administration Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real World Data, Chengdu, China
| | - Yan Ren
- Chinese Evidence-Based Medicine and Cochrane China Center, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu, China
- National Medical Products Administration Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real World Data, Chengdu, China
| | - Minghong Yao
- Chinese Evidence-Based Medicine and Cochrane China Center, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu, China
- National Medical Products Administration Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real World Data, Chengdu, China
| | - Guowei Li
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Canada
- Center for Clinical Epidemiology and Methodology, Guangdong Second Provincial General Hospital, Guangzhou, China
- Biostatistics Unit, Research Institute at St. Joseph's Healthcare Hamilton, Hamilton, Canada
| | - Kevin Lu
- South Carolina College of Pharmacy, University of South Carolina, Columbia, Columbia, South Carolina, USA
| | - Kang Zou
- Chinese Evidence-Based Medicine and Cochrane China Center, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu, China
- National Medical Products Administration Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real World Data, Chengdu, China
| | - Wen Wang
- Chinese Evidence-Based Medicine and Cochrane China Center, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu, China
- National Medical Products Administration Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real World Data, Chengdu, China
| | - Xin Sun
- Chinese Evidence-Based Medicine and Cochrane China Center, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu, China
- National Medical Products Administration Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China
- Sichuan Center of Technology Innovation for Real World Data, Chengdu, China
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Conner SC, Trinquart L. Estimation and modeling of the restricted mean time lost in the presence of competing risks. Stat Med 2021; 40:2177-2196. [PMID: 33567477 DOI: 10.1002/sim.8896] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 01/11/2021] [Accepted: 01/12/2021] [Indexed: 12/14/2022]
Abstract
Survival data with competing or semi-competing risks are common in observational studies. As an alternative to cause-specific and subdistribution hazard ratios, the between-group difference in cause-specific restricted mean times lost (RMTL) gives the mean difference in life expectancy lost to a specific cause of death or in disease-free time lost, in the case of a nonfatal outcome, over a prespecified period. To adjust for covariates, we introduce an inverse probability weighted estimator and its variance for the marginal difference in RMTL. We also introduce an inverse probability of censoring weighted regression model for the RMTL. In simulation studies, we examined the finite sample performance of the proposed methods under proportional and nonproportional subdistribution hazards scenarios. We illustrated both methods with competing risks data from the Framingham Heart Study. We estimated sex differences in atrial fibrillation (AF)-free times lost over 40 years. We also estimated sex differences in mean lifetime lost to cardiovascular disease (CVD) and non-CVD death over 10 years among individuals with AF.
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Affiliation(s)
- Sarah C Conner
- Department of Biostatistics, Boston University, Boston, Massachusetts, USA
| | - Ludovic Trinquart
- Department of Biostatistics, Boston University, Boston, Massachusetts, USA
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