A Bayesian Network Meta-analysis for Binary Outcome: A Simulation Study

Abstract

<p>Meta-analysis is a method of synthesizing results of different studies conducted to answer a specific question. Meta-analysis applications have been published in a wide range of disciplines including medicine, education, psychology and many others. However, for many years, only pair-wise and direct comparisons have been made using standard meta-analysis methods. It is only recently that network meta-analysis emerged enabling the comparison of multiple treatments based on estimates from different studies. With network meta-analysis, the relative efficacy (or safety) of a particular intervention versus competing interventions can be obtained even in the absence of head-to-head evidence via a common comparator.</p> <p>An increasing number of methodologies related to network meta-analysis, assessments of underlying assumptions, and strategies for presentation of results have been proposed by several researchers. But only few simulation studies have been done to investigate different characteristics of this emerging statistical method. Hierarchical Bayesian meta-analysis model is commonly used in network meta-analysis to estimate effect of each intervention relative to every other. This model facilitates the calculation of the rank probabilities of a set of alternative treatments. However, various factors can determine the performance of the model which needs to be considered before using results for decision.</p> <p>This project aimed to investigate how the Bayesian hierarchical model estimates the rank probability of the best overall most effective treatment (i.e., the treatment ranked first) under different scenarios for modelling a binary outcome. Different network geometries, numbers of studies per comparison, sets of probabilities of success for treatments and sample sizes were investigated in our simulation study for binary outcome.</p> <p>Our simulation study showed that the estimates of treatments under consideration can be affected by network structures. Similar geometries affect the estimate in similar ways. Unbalanced number of studies per comparison influenced estimates of treatments in the geometries we considered. When a superior treatment is involved in the network, the hierarchical Bayesian mixed treatment model correctly identified it regardless of network patterns, number of studies and individual study sample size.</p>