Comparison of Statistical Models for Crossover design Crossover designs have been widely used in clinical trials to investigate the efficacy of new treatments. In crossover design, each subject is treated subsequently with different treatments. Many methods such as linear mixed models (LMMs) and generalized estimating equation (GEE) models have been used to analyze the repeated measurements from crossover design. When we consider repeated measured response variables, estimation of random components for LMMs is not always easy. In this article, we applied the GEE method to crossover design to overcome the limitation of LMMs. To apply the GEE model to the data from the crossover designs, we need to switch the role of variables in LMM such a way that the independent variable in LMMs is considered as a response variable in GEE model and vice versa. The purpose of this study is to compare the performance of these GEE models and LMMs for crossover designs. Through simulation studies, we checked the type I errors and compared power to evaluate the performance of the proposed GEE model and LMMs.

Singlereplicate longitudinal data analysis in the presence of multiple instrumental measurement errors Abstract   In this paper, a novel method as a combination of the expectationmaximization (EM) algorithm and Variogram is proposed to decompose the longitudinal measurement errors in the absence of replications and the presence of multiple instrumental measurement errors. In the proposed method, multiple measurements are considered where the units are observed by several distinct instruments (gauges). The approach decouples the observed variance of the measurement model into the process and measurement system variances. In addition, it decomposes the variance of multiple instruments into the process and instrument variances. In the end, the proposed model is validated and tested based on simulated longitudinal data as well as a real case study related to the Framingham Heart study, measuring systolic blood pressure by multiple instruments. In addition, the robustness of the proposed method to the missing values, a common problem in longitudinal data, is demonstrated.