Speaker
Description
Repeated measures data are commonly encountered in a wide variety of disciplines including business, agriculture and medicine. They entail collection of multiple measurements from the same unit or subject over time, space or both. The fact that observations from the same unit will not be independent poses particular challenges to the statistical procedures used for the analysis of such data. Longitudinal data is a special case of repeated measures. In a longitudinal context, data are clustered within patients, thus, a random effect remains constant within a patient but changes across patients. Mixed models for repeated measures (MMRM) are suited for modeling continuous outcomes measured at discrete time points or within defined time-windows, hence applicable in balanced designs such as randomized control trials (RCT), utilizing time as a categorical factor. Typically, MMRM specifies no patient level random effects, but instead models the correlation within the repeated measures over time through unstructured correlation matrix of residual errors. With highly unbalanced designs, MMRM may encounter considerable challenges associated with cross-level bias. If measurements occur on a more ad-hoc basis, such that times of measurement vary across subjects, it may no longer be feasible to use MMRM. Even with balanced RCT designs, the choice of treating time as a categorical factor or a continuous variable depends on the research goal. If one is interested in studying the functional relationship between the outcome and time, it is appropriate to treat time as a continuous variable, hence not feasible within MMRM. Linear mixed-effects (LME) models consider both fixed and random effects, hence allows considerable modeling flexibility. In our case study, we analyze data for a 2 treatments by 2 periods crossover trial, within MMRM and LME modeling frameworks; applying Grizzles model, James & Kenward model and piecewise linear model.
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