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Activity Number: 247 - Causal Inference and Statistical Learning of Intervention and Policy Effects
Type: Contributed
Date/Time: Wednesday, August 11, 2021 : 10:00 AM to 11:50 AM
Sponsor: Health Policy Statistics Section
Abstract #317717
Title: Bayesian Multivariate Network Meta-Analysis Model for the Difference in Restricted Mean Survival Times
Author(s): Xiaoyu Tang* and Ludovic Trinquart
Companies: Department of Biostatistics, Boston Uiversity School of Public Health and Department of Biostatistics, Boston University School of Public Health
Keywords: Network Meta-Analysis; Restricted Mean Survival Time; Survival Analysis; Randomized Controlled Trials; Clinical Trials as Topic; Non-Small Cell Lung Cancer

NMA enables inference for all pair-wise comparisons between interventions available for the same indication, by using both direct evidence and indirect evidence. In randomized trials with time-to event outcome data, conventional NMA methods rely on the hazard ratio and the proportional hazards assumption, and ignore the varying follow-up durations across trials. We introduce a novel multivariate NMA model for the difference in restricted mean survival times (RMST). Our model synthesizes all the available evidence from multiple time points simultaneously and borrows information across time points through within-study covariance and between-study covariance for the differences in RMST. We estimated the model under the Bayesian framework. The simulation study indicates that our multiple-timepoint model yields lower mean squared error over the conventional single-timepoint model at all time points. We illustrated the model on a network of randomized trials of second-line treatments of advanced non-small-cell lung cancer. Our multiple-timepoint model yielded increased precision and detected evidence of benefit at earlier timepoints as compared to the single-timepoint model.

Authors who are presenting talks have a * after their name.

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