Guolo A
University of Verona, via dell'Artigliere 19, I-37129, Verona, Italy.
Stat Med. 2014 May 30;33(12):2062-76. doi: 10.1002/sim.6076. Epub 2013 Dec 15.
This paper investigates the use of SIMEX, a simulation-based measurement error correction technique, for meta-analysis of studies involving the baseline risk of subjects in the control group as explanatory variable. The approach accounts for the measurement error affecting the information about either the outcome in the treatment group or the baseline risk available from each study, while requiring no assumption about the distribution of the true unobserved baseline risk. This robustness property, together with the feasibility of computation, makes SIMEX very attractive. The approach is suggested as an alternative to the usual likelihood analysis, which can provide misleading inferential results when the commonly assumed normal distribution for the baseline risk is violated. The performance of SIMEX is compared to the likelihood method and to the moment-based correction through an extensive simulation study and the analysis of two datasets from the medical literature.
本文研究了基于模拟的测量误差校正技术SIMEX在涉及将对照组中受试者的基线风险作为解释变量的研究的荟萃分析中的应用。该方法考虑了影响治疗组中结果信息或每项研究中可用的基线风险的测量误差，同时无需对真实未观察到的基线风险的分布做任何假设。这种稳健性以及计算的可行性使得SIMEX非常有吸引力。该方法被建议作为通常似然分析的替代方法，当基线风险的常见正态分布假设被违反时，似然分析可能会提供误导性的推断结果。通过广泛的模拟研究以及对医学文献中两个数据集的分析，将SIMEX的性能与似然方法和基于矩的校正方法进行了比较。
