It has been argued that the models used to analyze data from crossover designs are not appropriate when simple carryover effects are assumed. In this paper, the estimability conditions of the carryover effects are found, and a theoretical result that supports them, additionally, two simulation examples are developed in a non-linear dose-response for a repeated measures crossover trial in two designs: the traditional AB/BA design and a Williams square. Both show that a semiparametric model can detect complex carryover effects and that this estimation improves the precision of treatment effect estimators. We 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 either, the absence of carryover or simplex carryover effects. In addition, an application of the methodology is shown and the richness in analysis that is gained by being able to estimate complex carryover effects is evident.

翻译：已有研究指出，假设简单残留效应的模型在分析交叉设计数据时并不恰当。本文确定了残留效应的可估计条件及其理论支持，并通过两个非线性剂量-响应模拟示例，在重复测量交叉试验的两种设计（传统AB/BA设计与Williams方设计）中展开分析。结果表明，半参数模型能够检测复杂残留效应，且该估计提高了处理效应估计量的精度。我们得出结论：当每个个体每个观察周期内至少存在五次重复测量时，半参数统计模型能提供良好的处理效应估计值，并能降低假设无残留效应或仅存在简单残留效应模型所带来的偏倚。此外，本文展示了该方法的应用实例，并明确了通过估计复杂残留效应所获得的丰富分析价值。