Reporting, handling, and interpretation of time-varying drug treatments in observational studies using routinely collected healthcare data

Wen Wang; Qiao He; Jiayue Xu; Mei Liu; Mingqi Wang; Qianrui Li; Xia Zhang; Yunxiang Huang; Yuanjin Zhang; Ling Li; Kang Zou; Guowei Li; Kevin Lu; Pei Gao; Feng Chen; Jeff Jianfei Guo; Min Yang; Xin Sun

doi:10.1111/jebm.12577

Reporting, handling, and interpretation of time-varying drug treatments in observational studies using routinely collected healthcare data

J Evid Based Med. 2023 Dec;16(4):495-504. doi: 10.1111/jebm.12577. Epub 2023 Dec 18.

Authors

Wen Wang^{1

2

3}, Qiao He^{1

2

3}, Jiayue Xu^{1

2

3}, Mei Liu^{1

2

3}, Mingqi Wang^{1

2

3}, Qianrui Li^{1

2

3}, Xia Zhang^{1

2

3}, Yunxiang Huang^{1

2

3}, Yuanjin Zhang^{1

2

3}, Ling Li^{1

2

3}, Kang Zou^{1

2

3}, Guowei Li^{4

5}, Kevin Lu⁶, Pei Gao⁷, Feng Chen⁸, Jeff Jianfei Guo⁹, Min Yang^{10

11}, Xin Sun^{1

2

3}

Affiliations

¹ Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
² National Medical Products Administration Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, Sichuan, China.
³ Sichuan Center of Technology Innovation for Real World Data, Chengdu, Sichuan, China.
⁴ Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada.
⁵ Center for Clinical Epidemiology and Methodology, Guangdong Second Provincial General Hospital, Guangzhou, Guangdong, China.
⁶ South Carolina College of Pharmacy, University of South Carolina Columbia, Columbia, South Carolina, USA.
⁷ Department of Epidemiology and Biostatistics, Peking University Health Science Center, Beijing, China.
⁸ Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
⁹ College of Pharmacy, University of Cincinnati, Cincinnati, Ohio, USA.
¹⁰ Department of Epidemiology and Biostatistics, West China School of Public Health, Sichuan University, Chengdu, Sichuan, China.
¹¹ Faculty of Health, Design and Art, Swinburne Technology University, Victory, Australia.

PMID: 38108104
DOI: 10.1111/jebm.12577

Abstract

Background: Time-varying drug treatments are common in studies using routinely collected health data (RCD) for assessing treatment effects. This study aimed to examine how these studies reported, handled, and interpreted time-varying drug treatments.

Methods: A systematic search was conducted on PubMed from 2018 to 2020. Eligible studies were those used RCD to explore drug treatment effects. We summarized the reporting characteristics and methods employed for handling time-varying treatments. Logistic regressions were performed to investigate the association between study characteristics and the reporting of time-varying treatments.

Results: Two hundred and fifty-six studies were included, and 225 (87.9%) studies involved time-varying treatments. Of these, 24 (10.7%) reported the proportion of time-varying treatments and 105 (46.7%) reported methods used to handle time-varying treatments. Multivariable logistic regression showed that medical studies, prespecified protocol, and involvement of methodologists were associated with a higher likelihood of reporting the methods applied to handle time-varying treatments. Among the 105 studies that reported methods, as-treated analyses were the most commonly used analysis sets, which were employed in 73.9%, 75.3% and 88.2% of studies that reported approaches for treatment discontinuation, treatment switching and treatment add-on. Among the 225 studies involved time-varying treatments, 27 (12.0%) acknowledged the potential bias introduced by treatment change, of which 14 (51.9%) suggested that potential biases may impact acceptance or rejection of the null hypothesis.

Conclusions: Among observational studies using RCD, the underreporting about the presence and methods for handling time-varying treatments was largely common. The potential biases due to time-varying treatments have frequently been disregarded. Collaborative endeavors are strongly needed to enhance the prevailing practices.

Keywords: routinely collected data; time-varying treatments; treatment effect.

Reporting, handling, and interpretation of time-varying drug treatments in observational studies using routinely collected healthcare data

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