DeSantis Stacia M, Zhu Huirong
Division of Biostatistics, School of Public Health, University of Texas Health Science Center, Houston, TX, USA (SMD, HZ).
Med Decis Making. 2014 Oct;34(7):899-910. doi: 10.1177/0272989X14537558. Epub 2014 Jun 16.
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.
几种治疗酒精依赖的方法已在随机对照试验中进行了测试，从而产生了具有证据网络结构的系统评价，即混合治疗比较（MTC）。在该网络中，活性治疗之间的直接比较很少。到目前为止，这个网络尚未得到充分分析。例如，治疗之间的“间接比较”（例如，通过A:B和A:C试验的估计值获得的B:C治疗比较）尚未纳入治疗效果的估计中。这对未来随机对照试验的规划有影响。
我们应用贝叶斯MTC荟萃分析的最新进展来分析证据网络。利用这些结果，我们提出了一种方法，以便在更新的荟萃分析背景下，为很少被比较且因此其效应大小未通过荟萃分析得到充分了解的治疗方法的假设试验提供信息、进行设计并确定样本量。
与成对荟萃分析相比，MTC荟萃分析提供了更准确的估计，并揭示了很少直接比较的活性治疗之间的决定性差异。对所有结果进行加权表明，联合治疗（纳曲酮+阿坎酸）具有成为“最佳”治疗方法的最高后验概率。如果要对联合疗法与单独使用阿坎酸进行一项新的临床试验，没有可行的样本量会导致决定性的荟萃分析。
应使用MTC荟萃分析来估计直接和间接证据一致的网络中的治疗效果，并为未来研究的设计提供信息。
