Applying the law of iterated logarithm to control type I error in cumulative meta-analysis of binary outcomes

作者：Mingxiu Hu, Joseph C. Cappelleri, K. K. Gordon Lan · 发表于：Clinical Trials · 年份：2007 · DOI：10.1177/1740774507081219 · 被引用次数：62 · 研究领域：Meta-analysis and systematic reviews、Statistical Methods in Clinical Trials、Data Analysis with R

BACKGROUND: Cumulative meta-analysis typically involves performing an updated meta-analysis every time when new trials are added to a series of similar trials, which by definition involves multiple inspections. Neither the commonly used random effects model nor the conventional group sequential method can control the type I error for many practical situations. In our previous research, Lan et al. (Lan KKG, Hu M-X, Cappelleri JC. Applying the law of iterated logarithm to cumulative meta-analysis of a continuous endpoint. Statistica Sinica 2003; 13: 1135-45) proposed an approach based on the law of iterated logarithm (LIL) to this problem for the continuous case. PURPOSE: The study is an extension and generalization of our previous research to binary outcomes. Although it is based on the same LIL principle, we found the discrete case much more complex and the results from the continuous case do not apply to the binary case. The simulation study presented here is also more extensive. METHODS: The LIL based method ;penalizes' the Z-value of the test statistic to account for multiple tests and for the estimation of heterogeneity in treatment effects across studies. It involves an adjustment factor, which is directly related to the control of type I error and determined through extensive simulations under various conditions. RESULTS: With an adjustment factor of 2, the LIL-based test statistics controls the overall type I error when odds ratio or relative risk is the parameter of i...