A Bayesian mixed-treatment comparison meta-analysis of treatments for alcohol dependence and implications for planning future trials.

Abstract

Several treatments for alcohol dependence have been tested in randomized controlled trials, giving rise to systematic reviews with a network of evidence structure, or mixed treatment comparisons (MTCs). Within the network, there are few direct comparisons of active treatments. Thus far, this network has not been adequately analyzed. For example, "indirect comparisons" between treatments (e.g., the comparison of treatments B:C obtained via estimates from A:B and A:C trials) have not been incorporated into estimates of treatment effects. This has implications for the planning of future randomized controlled trials. We applied recent developments in Bayesian MTC meta-analysis to analyze the network of evidence. Using these results, we proposed a methodology to inform, design, and power a hypothetical trial in the context of an updated meta-analysis for treatments that have been infrequently compared and therefore whose effect sizes are not well informed by a meta-analysis. An MTC meta-analysis provides more accurate estimates than a pairwise meta-analysis and uncovers decisive differences between active treatments that have been infrequently directly compared. Weighting across all outcomes indicates that a combination (naltrexone + acamprosate) treatment has the highest posterior probability of being the "best" treatment. If a new clinical trial were to be conducted of a combination therapy versus acamprosate alone, there is no feasible sample size that would result in a decisive meta-analysis. An MTC meta-analysis should be used to estimate treatment effects in networks in which direct and indirect evidence are consistent and to inform the design of future studies.