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D ‐optimal designs for multiarm trials with dropouts
Author(s) -
Lee Kim May,
Biedermann Stefanie,
Mitra Robin
Publication year - 2019
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8148
Subject(s) - dropout (neural networks) , clinical study design , clinical trial , research design , computer science , design of experiments , population , treatment effect , medicine , statistics , machine learning , mathematics , environmental health , pathology , traditional medicine
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.