Optimum Design of Disease-modifying Trials on Alzheimer's Disease.

Randomized start and withdrawal designs have been recently proposed to test the disease-modifying agents on Alzheimer's disease (AD). This article provides methods to determine the optimum parameters for these designs. A general linear mixed effects model is proposed. This model employs a piecewise linear growth pattern for those in the delayed treatment or early withdrawal arm, and incorporates a potential correlation on the rates of change on efficacy outcome before and after the treatment switch. Based on this model, we formulate the disease-modifying hypothesis by comparing the rate of change on efficacy outcome between treatment arms with and without a treatment switch, and develop a methodology to optimally determine the sample size allocations to different treatment arms as well as the time of treatment switch for subjects whose treatment is changed. We then propose an intersection-union test (IUT) to assess the disease-modifying efficacy, and study the size and the power of the IUT. Finally, we employ two recently published symptomatic trials on AD to obtain pilot estimates to model parameters, and provide the optimum design parameters including total and individual sample size to different arms as well as the time of treatment switch for future disease-modifying trials on AD.