Network Meta-Analysis With Individual Participant-Level Data of Time-to-Event Outcomes Using Cox Regression.

The accessibility of individual participant-level data (IPD) enhances the evaluation of moderation effects of patient covariates. It facilitates the provision of accurate estimation of intervention effects and confidence intervals by incorporating covariate correlations across multiple clinical trials. With a time-to-event outcome, Cox regression can be applied for network meta-analysis (NMA) using IPD. However, there lacks comprehensive reviews and comparisons of the specifications and assumptions of these Cox models and their impact on the interpretation of hazard ratios, effect moderation, and trial heterogeneity in IPD-NMA. In this paper, we examine various Cox models for IPD-NMA and compare different approaches to modeling trial, treatment, and covariate effects. We employ multiple graphical tools and statistical tests to assess proportional hazard assumptions and discuss their implications. Additionally, we explore the application of extended Cox models when the proportional hazard assumption is violated. Practical guidance on interpreting and reporting NMA results is provided. A simulation study is conducted to compare the performance of different models. We illustrate the methods to conduct IPD-NMA through a real data example.

Duke Scholars

Author Christopher Bull Granger Medicine, Cardiology

Author Daniel Wojdyla  

Author Xiaofei Wang Biostatistics & Bioinformatics, Division of Biostatistics

Author Hwanhee Hong Biostatistics & Bioinformatics, Division of Biostatistics