D-optimal designs for multiarm trials with dropouts

Kim May Lee, S Biedermann, R Mitra

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)2749-2766
Number of pages18
JournalStatistics in Medicine
Issue number15
Publication statusPublished - 10 Jul 2019


  • available case analysis
  • design of experiments
  • linear mixed models
  • noninformative dropouts


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