Network meta-analysis: a statistical physics perspective

被引:0
|
作者
Davies, Annabel L. [1 ]
Galla, Tobias [2 ]
机构
[1] Univ Manchester, Sch Nat Sci, Dept Phys & Astron, Manchester, England
[2] Inst Fis Interdisciplinar & Sistemas Complejos CS, IFISC, Campus Univ Illes Balears, Palma De Mallorca 07122, Spain
基金
英国工程与自然科学研究理事会;
关键词
statistical inference; classical Monte Carlo simulations; stochastic processes; inference of graphical models; MAXIMUM-LIKELIHOOD-ESTIMATION; BETWEEN-STUDY HETEROGENEITY; GENERALIZED LEAST-SQUARES; VARIANCE ESTIMATORS; LAIRDS METHODOLOGY; BAYESIAN-INFERENCE; INFORMATION-THEORY; CLINICAL-TRIALS; INCONSISTENCY; MODEL;
D O I
10.1088/1742-5468/ac9463
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
Network meta-analysis (NMA) is a technique used in medical statistics to combine evidence from multiple medical trials. NMA defines an inference and information processing problem on a network of treatment options and trials connecting the treatments. We believe that statistical physics can offer useful ideas and tools for this area, including from the theory of complex networks, stochastic modelling and simulation techniques. The lack of a unique source that would allow physicists to learn about NMA effectively is a barrier to this. In this article we aim to present the 'NMA problem' and existing approaches to it coherently and in a language accessible to statistical physicists. We also summarise existing points of contact between statistical physics and NMA, and describe our ideas of how physics might make a difference for NMA in the future. The overall goal of the article is to attract physicists to this interesting, timely and worthwhile field of research.
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页数:79
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