Toward Evidence Synthesis of Adverse Events in Imbalanced Time-to-Event Data

Zhen Peng; Jingyi Jiang; Lifeng Lin; Luis Furuya-Kanamori; Sheyu Li; Yoon Loke; Haitao Chu; Sunita Vohra; Chang Xu

doi:10.1111/jebm.70144

Journal of Evidence-Based Medicine ›› 2026, Vol. 19 ›› Issue (2) :e70144 DOI: 10.1111/jebm.70144

METHODOLOGY

Toward Evidence Synthesis of Adverse Events in Imbalanced Time-to-Event Data

Author information +

¹. Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, Hefei, China

². Drug Clinical Trial Institution, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China

³. College of Basic Medical Sciences, Second Military Medical University, Naval Medical University, Shanghai, China

⁴. Department of Epidemiology and Biostatistics, University of Arizona, Tucson, USA

⁵. UQ Center for Clinical Research, The University of Queensland, Herston, Australia

⁶. Department of Endocrinology and Metabolism, Division of Guideline and Rapid Recommendation, Cochrane China Centre, MAGIC China Centre, Chinese Evidence-Based Medicine Centre, West China Hospital, Sichuan University, Chengdu, China

⁷. Norwich Medical School, University of East Anglia, Norwich, UK

⁸. Statistical Research and Innovation, Global Biometrics and Data Management, Pfizer Inc, New York, USA

⁹. Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Canada

¹⁰. School of Public Health, Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, China

¹¹. Proof of Concept Center, Eastern Hepatobiliary Surgery Hospital, Third Affiliated Hospital, Second Military Medical University, Naval Medical University, Shanghai, China

xuchang2016@runbox.com

Show less

History +

PDF

Abstract

Background: The routine approach in evidence synthesis of adverse events is to estimate the odds ratio or risk ratio of each individual study and then synthesize the study-specific effects for a pooled average estimate, while seldom consider the potential imbalanced duration of exposures of study arms. This article aims to investigate the potential impact of imbalanced exposure time on harm effects.

Methods: We simulated individual participant time-to-event data based on Cox proportional hazard model, with Weibull function to reshape the distribution of the hazards. We further collapsed the data into aggregated one and fitting both hierarchical Binomial regression model and hierarchical Poisson regression model to estimate the pooled RR and incidence rate ratio (IRR). The percentage bias, mean squared error, and coverage probability were examined.

Results: Our results suggested that imbalanced exposure time between study arms can have substantial impact on the estimation of harm effects in evidence synthesis, especially when the extent of the imbalance exceeds 20%. Estimating an IRR to address the imbalanced exposure time only made sense for non-recurrent events when the between-study heterogeneity is small or moderate. A case study by 22 ongoing trials verified the potential biased estimation when exposure time was imbalanced between study arms.

Conclusions: It is inappropriate to ignoring exposure time when there is a large difference (> 20%) between study arms; while the IRR could be used in some cases, collecting individual participant data for evidence synthesis of adverse events for time-to-event data should be the primary consideration.

Keywords

adverse events / evidence synthesis / simulation / time-to-event data

Cite this article

Download citation ▾

Zhen Peng, Jingyi Jiang, Lifeng Lin, Luis Furuya-Kanamori, Sheyu Li, Yoon Loke, Haitao Chu, Sunita Vohra, Chang Xu. Toward Evidence Synthesis of Adverse Events in Imbalanced Time-to-Event Data. Journal of Evidence-Based Medicine, 2026, 19 (2) : e70144 DOI:10.1111/jebm.70144

登录浏览全文

4963

注册一个新账户忘记密码

References

Publishing order | Descend order by publishing year | Descend order by cited within

Rights & permissions

2026 Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd. All rights reserved, including rights for text and data mining and training of artificial intelligence technologies or similar technologies.