惩罚GEE用于重复测量交叉设计中的复杂残留效应
Penalized GEE for Complex Carry-Over in Repeated-Measures Crossover Designs
AI总结：
本文提出了一种基于惩罚GEE的模型，用于估计交叉设计中的复杂残留效应，通过模拟展示该方法能提高治疗效应估计的精度，减少偏差。
摘要：
长期以来，人们认为用于分析交叉设计数据的模型在假设简单残留效应时并不合适。此外，从未发现能估计交叉设计中复杂残留效应的统计模型。然而，本文发现了复杂残留效应的可估计条件及支持这些结果的理论结果。此外，开发了一个非线性剂量-反应测试中的模拟示例，用于典型的AB/BA交叉设计带重复测量。该模拟表明，半参数模型能够检测复杂残留效应，并且这种估计提高了治疗效应估计量的精度。结论指出，当每个个体每个观测时期至少有五次重复时，半参数统计模型能提供良好的治疗效应估计，并相对于假设无残留效应或简单残留效应的模型减少偏差。此外，展示了该方法的应用，并展示了通过估计复杂残留效应获得的分析成果。
英文摘要：
It has been argued for many years that models used to analyze data from crossover designs are not appropriate when simple carryover effects are assumed. Furthermore, a statistical model that could estimate complex carry-over effects in crossover designs had never been found. However, in this paper, the estimability conditions of the complex carryover effects and a theoretical result that supports them are found. In addition, a simulation example is developed in a non-linear dose-response test for a typical AB/BA crossover design with repeated measures. This simulation shows that a semiparametric model can detect complex carryover effects and that this estimation improves the precision of the estimators of the treatment effect. It is concluded that when there are at least five replicates in each observation period per individual, semiparametric statistical models provide a good estimator of the treatment effect and reduce bias with respect to models that assume the absence of carryover effects or simplex carryover effects. Furthermore, an application of the methodology is shown and the wealth of analysis gained by estimating complex carryover effects is evident.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
