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Activity Number: 341 - Random Effects and Mixed Models
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #307160 Presentation
Title: Construction of the Design Matrix for Generalized Linear Mixed-Effects Models in the Context of Clinical Trials of Treatment Sequences
Author(s): Francisco Diaz*
Companies: The University of Kansas Medical Center
Keywords: Augmented regression; Estimability; Generalized inverses; Random effects linear models; Identifiability; Placebo

The estimation of carry-over effects is a difficult problem in the design and analysis of clinical trials of treatment sequences including cross-over trials. Except for simple designs, carry-over effects are usually unidentifiable. Also, washout periods are not always feasible or ethical. Designs with unidentifiable parameters do not have design matrices of full rank. We propose approaches to the construction of design matrices of full rank, without imposing artificial constraints on the carry-over effects. We present a new model for the design and analysis of clinical trials of treatment sequences, called Antichronic System, and introduce some special sequences called Skip Sequences. Carry-over effects are identifiable only if appropriate Skip Sequences are used in the design and/or data analysis of the clinical trial. We explain how Skip Sequences can be implemented in practice, and present a method of computing the appropriate Skip Sequences. We show applications to the design of a cross-over study with 3 treatments and 3 periods, and to the data analysis of the STAR*D study of sequences of treatments for depression. (Diaz FJ, 2018, Colombian Journal of Statistics 41, 191-233.)

Authors who are presenting talks have a * after their name.

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