D-optimal designs for multiarm trials with dropouts

Lee, Kim May; Biedermann, Stefanie and Mitra, Robin (2019). D-optimal designs for multiarm trials with dropouts. Statistics in Medicine, 38(15) pp. 2749–2766.

DOI: https://doi.org/10.1002/sim.8148

Abstract

Multiarm trials with follow‐up on participants are commonly implemented to assess treatment effects on a population over the course of the studies. Dropout is an unavoidable issue especially when the duration of the multiarm study is long. Its impact is often ignored at the design stage, which may lead to less accurate statistical conclusions. We develop an optimal design framework for trials with repeated measurements, which takes potential dropouts into account, and we provide designs for linear mixed models where the presence of dropouts is noninformative and dependent on design variables. Our framework is illustrated through redesigning a clinical trial on Alzheimer's disease, whereby the benefits of our designs compared with standard designs are demonstrated through simulations.

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About

• Item ORO ID
• 72257
• Item Type
• Journal Item
• ISSN
• 0277-6715
• Keywords
• available case analysis; design of experiments; linear mixed models; noninformative dropouts
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Copyright Holders
• © 2019 The Authors
• Depositing User
• Stefanie Biedermann