Linear combinations of multiple outcome measures to improve the power of efficacy analysis – application to clinical trials on early-stage Alzheimer's disease

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Abstract

Modern clinical trials on Alzheimer's disease (AD) focus on the early symptomatic stage or even the preclinical stage. Subtle disease progression at the early stages, however, poses a major challenge in designing such clinical trials. We propose a multivariate mixed model on repeated measures to model the disease progression over time on multiple efficacy outcomes, and derive the optimum weights to combine multiple outcome measures by minimizing the sample sizes to adequately power the clinical trials. A cross-validation simulation study is conducted to assess the accuracy for the estimated weights as well as the improvement in reducing the sample sizes for such trials. The proposed methodology is applied to the multiple cognitive tests from the ongoing observational study of the Dominantly Inherited Alzheimer Network (DIAN) to power future clinical trials in the DIAN with a cognitive endpoint. Our results show that the optimum weights to combine multiple outcome measures can be accurately estimated, and that compared to the individual outcomes, the combined efficacy outcome with these weights significantly reduces the sample size required to adequately power clinical trials. When applied to the clinical trial in the DIAN, the estimated linear combination of six cognitive tests can adequately power the clinical trial.

Original languageEnglish
Pages (from-to)36-58
Number of pages23
JournalBiostatistics and Epidemiology
Volume1
Issue number1
DOIs
StatePublished - Jan 1 2017

Keywords

  • Alzheimer's disease
  • cross-validation
  • multivariate mixed model for repeated measures (MMMRM)
  • power
  • sample size

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