A studentized permutation test for the treatment effect in individual participant data meta-analysis
个体参与者数据元分析中处理效应的学生化置换检验AI 解读 ↓
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个体参与者数据元分析中处理效应的学生化置换检验
- 研究问题
- 在研究数量较少的个体参与者数据元分析中，如何对总体处理效应进行检验与区间估计，兼顾第一类错误及区间长度？
- 方法与思路
- 作者在线性混合模型框架下跨研究置换标准化残差，构造学生化检验，并通过置换分位数及显著性边界搜索形成两类区间。
- 材料陈述的贡献
- 摘要称模拟中的检验和区间表现令人满意，相比若干竞争方法通常得到更短的置信区间，旨在改善小研究数下的推断。
- 阅读关注点
- 核对跨研究残差置换的有效条件、学生化方式、两类区间的覆盖率，以及模拟是否涵盖研究异质性和分布违背。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核实；摘要未说明置换有效性的完整条件，不能据此保证任意设计下错误率受控。
原文摘要
Meta-analysis is a well-established tool used to combine data from several independent studies, each of which usually compares the effect of an experimental treatment with a control group. While meta-analyses are often performed using aggregated study summaries, they may also be conducted using individual participant data (IPD). Classical meta-analysis models may be generalized to handle continuous IPD by formulating them within a linear mixed model framework. IPD meta-analyses are commonly based on a small number of studies. Technically, inference for the overall treatment effect can be performed using Student-t approximation. However, as some approaches may not adequately control the type I error, Satterthwaite's or Kenward-Roger's method have been suggested to set the degrees-of-freedom parameter. The latter also adjusts the standard error of the treatment effect estimator. Nevertheless, these methods may be conservative. Since permutation tests are known to control the type I error and offer robustness to violations of distributional assumptions, we propose a studentized permutation test for the treatment effect based on permutations of standardized residuals across studies in IPD meta-analysis. Also, we construct confidence intervals for the treatment effect based on this test. The first interval is derived from the percentiles of the permutation distribution. The second interval is obtained by searching values closest to the effect estimate that are just significantly different from the true effect. In a simulation study, we demonstrate satisfactory performance of the proposed methods, often producing shorter confidence intervals compared with competitors.