Development of novel statistical methods to detect treatment effect in Alzheimer’s disease clinical trials over time

Lead Investigator: Sheng Luo, Duke University
Title of Proposal Research: Development of novel statistical methods to detect treatment effect in Alzheimer’s disease clinical trials over time
Vivli Data Request: 8438
Funding Source: None
Potential Conflicts of Interest: None

Summary of the Proposed Research:
Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disorder that affects 10.7% of people older than 65 in the United States alone. An estimated 6.5 million Americans aged 65 and older are living with Alzheimer’s dementia in 2022.
AD causes impairment in multiple domains (e.g., cognitive, and behavioral). The disease progresses heterogeneously in time and across domains and individuals: decline may be observed in some, but not all health outcomes at any given time interval and the trajectory of progression may vary between different domains, both within and across AD patients. Therefore, no single health outcome reliably reflects the full spectrum of the disease severity and progression. Clinical trials of AD repeatedly collect multiple health outcomes to obtain an overview of disease progression of AD patients. To objectively assess the treatment efficacy in clinical trials, one needs to account for the trajectory of the treatment effect in terms of multiple outcomes overtime. There are three limitations of current research in assessing the treatment efficacy: (1) the test procedures are usually applied to the change outcome data from baseline to the last observation to test the treatment efficacy, ignoring the treatment effect and observed data over the whole treatment period; (2) most available tests are not directional. Those tests focus on whether there are differences between two groups and cannot tell if treatment is efficacious as compared to the placebo; and (3) the traditional test procedures are based on normal distribution assumptions on outcome variables. Hence, these tests are not appropriate for outcomes that are of different types (e.g., ordinal variable such as pain scale from 0 to 10, binary yes/no questions).

Therefore, we aim to develop novel statistical methods to detect treatment efficacy in AD clinical trials with multiple outcomes over time. The proposed test will compare the sums of ranking of each outcome variable at each time point for all individuals in either treatment or control groups. This test procedure does not require any distribution assumptions (e.g., normal distribution) on the outcome variables and it objectively evaluates the effect of a new therapy overtime by fully utilizing the whole trajectories of the multiple outcomes. Furthermore, we aim to develop novel methodologies to address relevant issues in AD clinical trial designs under the proposed test framework.

Requested Studies:
A Phase 2 Multiple Dose, Multicenter, Randomized, Double-Blind, Placebo-Controlled Study to Evaluate the Efficacy and Safety of ABBV-8E12 in Subjects With Early Alzheimer’s Disease
Data Contributor: AbbVie
Study ID: NCT02880956
Sponsor ID: M15-566

An Extension Study of ABBV-8E12 in Early Alzheimer’s Disease
Data Contributor: AbbVie
Study ID: NCT03712787
Sponsor ID: M15-570