PALM: PATIENT-CENTERED TREATMENT RANKING VIA LARGE-SCALE MULTIVARIATE NETWORK META-ANALYSIS

• 成果类型: Article
• 署名作者: Duan, Rui; Tong, Jiayi; Lin, Lifeng; Levine, Lisa; Sammel, Mary; Stoddard, Joel; Li, Tianjing; Schmid, Christopher H.; Chu, Haitao; Chen, Yong
• 署名单位: Harvard University; Harvard T.H. Chan School of Public Health; University of Pennsylvania; University of Arizona; University of Pennsylvania; University of Colorado System; University of Colorado Denver; Brown University; Pfizer; Pfizer USA
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1652
• 发表日期: 2023
• 页码: 815-837
• 关键词: multiple-treatments metaanalysis; outcome reporting bias; regression-analysis; longitudinal data; inconsistency; pharmacokinetics; inference; HEALTH; MODEL; RISK

摘要

The growing number of available treatment options has led to urgent needs for reliable answers when choosing the best course of treatment for a patient. As it is often infeasible to compare a large number of treatments in a single randomized controlled trial, multivariate network meta-analyses (NMAs) are used to synthesize evidence from trials of a subset of the treatments, where both efficacy and safety related outcomes are considered simultaneously. However, these large-scale multiple-outcome NMAs have created challenges to existing methods due to the increasing complexity of the unknown correlations between outcomes and treatment comparisons. In this paper, we proposed a new framework for PAtient-centered treatment ranking via Large-scale Multivariate network meta-analysis, termed as PALM, which includes a parsimonious modeling approach, a fast algorithm for parameter estimation and inference, a novel visualization tool for presenting multivariate outcomes, termed as the origami plot, as well as personalized treatment ranking procedures taking into account the individual's considerations on multiple outcomes. In application to an NMA that compares 14 treatment options for labor induction, we provided a comprehensive illustration of the proposed framework and demonstrated its computational efficiency and practicality, and we obtained new insights and evidence to support patient-centered clinical decision making.