Abstract
Network meta-analysis (NMA) is a statistical technique for the comparison of treatment options. Outcomes of Bayesian NMA include estimates of treatment effects, and the probabilities that each treatment is ranked best, second best and so on. How exactly network topology affects the accuracy and precision of these outcomes is not fully understood. Here we carry out a simulation study and find that disparity in the number of trials involving different treatments leads to a systematic bias in estimated rank probabilities. This bias is associated with an increased variation in the precision of treatment effect estimates. Using ideas from the theory of complex networks, we define a measure of "degree irregularity" to quantify asymmetry in the number of studies involving each treatment. Our simulations indicate that more regular networks have more precise treatment effect estimates and smaller bias of rank probabilities. Conversely, these topological effects are not observed for the accuracy of treatment effect estimates. This reinforces the importance of taking into account multiple measures, rather than making decisions based on a single metric. We also find that degree regularity is a better indicator for the accuracy and precision of parameter estimates in NMA than both the total number of studies in a network and the disparity in the number of trials per comparison. These results have implications for planning future trials. We demonstrate that choosing trials which reduce the network's irregularity can improve the precision and accuracy of parameter estimates from NMA.
Highlights
Meta-analysis is an important statistical technique used to combine the results of multiple randomised controlled trials
In order to characterise network geometry we introduce a measure of asymmetry in the number of studies per treatment which we call “degree irregularity.”
Through simulations of multiple network geometries we investigate how this metric affects the precision and accuracy of the treatment effect estimates, and the quality of rank probability estimates and surface under the cumulative ranking” curve (SUCRA) values
Summary
Meta-analysis is an important statistical technique used to combine the results of multiple randomised controlled trials. Individual trials have small sample sizes and involve subjects taken from a reduced population. It is desirable to systematically integrate results from different trials that address the same clinical question. Over the last four decades meta-analysis has become invaluable for the comparison of treatment options.[1]. Conventional meta-analysis focuses on pairwise comparisons of treatments. Network meta-analysis (NMA) has emerged as a technique for
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