Network Meta-Analysis of Time-to-Event Endpoints With Individual Participant Data Using Restricted Mean Survival Time Regression

IF 1.3 3区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Kaiyuan Hua, Xiaofei Wang, Hwanhee Hong
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引用次数: 0

Abstract

Network meta-analysis (NMA) extends pairwise meta-analysis to compare multiple treatments simultaneously by combining “direct” and “indirect” comparisons of treatments. The availability of individual participant data (IPD) makes it possible to evaluate treatment effect moderation and to draw inferences about treatment effects by taking the full utilization of individual covariates from multiple clinical trials. In IPD-NMA, restricted mean survival time (RMST) models have gained popularity when analyzing time-to-event outcomes because RMST models offer more straightforward interpretations of treatment effects with fewer assumptions than hazard ratios commonly estimated from Cox models. Existing approaches estimate RMST within each study and then combine by using aggregate-level NMA methods. However, these methods cannot incorporate individual covariates to evaluate the effect moderation. In this paper, we propose advanced RMST NMA models when IPD are available. Our models allow us to study treatment effect moderation and provide a comprehensive understanding about comparative effectiveness of treatments and subgroup effects. The methods are evaluated by an extensive simulation study and illustrated using a real NMA example about treatments for patients with atrial fibrillation.

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来源期刊
Biometrical Journal
Biometrical Journal 生物-数学与计算生物学
CiteScore
3.20
自引率
5.90%
发文量
119
审稿时长
6-12 weeks
期刊介绍: Biometrical Journal publishes papers on statistical methods and their applications in life sciences including medicine, environmental sciences and agriculture. Methodological developments should be motivated by an interesting and relevant problem from these areas. Ideally the manuscript should include a description of the problem and a section detailing the application of the new methodology to the problem. Case studies, review articles and letters to the editors are also welcome. Papers containing only extensive mathematical theory are not suitable for publication in Biometrical Journal.
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