Department of Economics, University of Verona, Verona, Italy.
Stat Med. 2013 Jan 15;32(1):40-50. doi: 10.1002/sim.5506. Epub 2012 Jul 17.
This paper investigates a likelihood-based approach in meta-analysis of clinical trials involving the baseline risk as explanatory variable. The approach takes account of the errors affecting the measure of either the treatment effect or the baseline risk, while facing the potential misspecification of the baseline risk distribution. To this aim, we suggest to model the baseline risk through a flexible family of distributions represented by the skew-normal. We describe how to carry out inference within this framework and evaluate the performance of the approach through simulation. The method is compared with the routine likelihood approach based on the restrictive normality assumption for the baseline risk distribution and with the weighted least-squares regression. We apply the competing approaches to the analysis of two published datasets.
本文研究了一种基于似然的方法，用于分析涉及基线风险作为解释变量的临床试验的荟萃分析。该方法考虑了影响治疗效果或基线风险测量的误差，同时面临基线风险分布潜在的误指定。为此，我们建议通过由斜态正态分布表示的灵活分布族来对基线风险进行建模。我们描述了如何在这个框架内进行推断，并通过模拟来评估该方法的性能。该方法与基于基线风险分布的限制性正态性假设的常规似然方法以及加权最小二乘法回归进行了比较。我们将竞争方法应用于两个已发表数据集的分析。
