Research Article: Power analysis to detect treatment effects in longitudinal clinical trials for Alzheimer’s disease

Date Published: May 24, 2017

Publisher: Elsevier

Author(s): Zhiyue Huang, Graciela Muniz-Terrera, Brian D.M. Tom.

http://doi.org/10.1016/j.trci.2017.04.007

Abstract

Assessing cognitive and functional changes at the early stage of Alzheimer’s disease (AD) and detecting treatment effects in clinical trials for early AD are challenging.

Under the assumption that transformed versions of the Mini–Mental State Examination, the Clinical Dementia Rating Scale–Sum of Boxes, and the Alzheimer’s Disease Assessment Scale–Cognitive Subscale tests’/components’ scores are from a multivariate linear mixed-effects model, we calculated the sample sizes required to detect treatment effects on the annual rates of change in these three components in clinical trials for participants with mild cognitive impairment.

Our results suggest that a large number of participants would be required to detect a clinically meaningful treatment effect in a population with preclinical or prodromal Alzheimer’s disease. We found that the transformed Mini–Mental State Examination is more sensitive for detecting treatment effects in early AD than the transformed Clinical Dementia Rating Scale–Sum of Boxes and Alzheimer’s Disease Assessment Scale–Cognitive Subscale. The use of optimal weights to construct powerful test statistics or sensitive composite scores/endpoints can reduce the required sample sizes needed for clinical trials.

Consideration of the multivariate/joint distribution of components’ scores rather than the distribution of a single composite score when designing clinical trials can lead to an increase in power and reduced sample sizes for detecting treatment effects in clinical trials for early AD.

Partial Text

Much effort has been devoted to developing disease-modifying treatments that intervene in the pathobiologic processes involved in the early stage of Alzheimer’s disease (AD). Any therapy that is effective at treating this early manifestation of the dementia process may provide an opportunity for managing the disease while patient function is relatively preserved [1]. Standard instruments used to quantify cognitive and functional decline in AD are relatively insensitive to the changes at early AD [2]. This raises challenges for assessing the early changes in cognition and function across the spectrum of AD [3] and makes detecting treatment effects in clinical trials for early AD even harder [2].

Table 3 presents the sample sizes required for each of the aforementioned weighting specifications and under the different trial duration scenarios when the statistical power is specified at 80% and the significance level is set at 5%. Also reported are the calculated sample sizes when each component is considered separately for powering the trial, and a Bonferroni correction is applied. Here, the maximum of the three calculated sample sizes based on the three components is chosen as the sample size to be specified for the trial.Table 3The sample sizes calculated by each approach with 80% statistical power and 5% significance level by trial durationTest statisticWeightsTrial duration2 years3 years4 years5 years6 yearsΞJ-23,714704129831550908ΞJC(w)w(1)24,934744731921678994w(2)45,25913,548578930301786w(3)45,84413,635578930141769wZ17,672524222161149672wJC*17,072506921481116654wC*17,139509021561120656ΞC(w)w(1)26,8518059345118091067w(2)46,52413,929594331051827w(3)47,65414,189601731261831wZ17,881530622421162679wJC*17,625523622141147671wC*17,5495212220511436692 years3 years4 years5 years6 yearsBonferroni correction63,56318,926802541702443NOTE. Numbers given in bold indicates the test statistic ΞJC(wJC∗) that gives the smallest sample sizes for each of the considered clinical trial design scenarios.

We have described three approaches for performing power analysis to detect treatment effects in clinical trials for early AD. From our investigations, we found that jointly modeling the component scores and then constructing sensitive test statistics or composite scores based on optimal weights will improve the efficiency of clinical trials. Under our model assumptions, testing based on the optimal composite treatment effect will lead to the smallest required sample sizes and therefore should be recommended when powering clinical trials in AD if treatment effects on multiple components are of interest.

 

Source:

http://doi.org/10.1016/j.trci.2017.04.007

 

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