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Who Benefited Most from the Internet-Based Conversational Engagement RCT (I-CONECT)? Application of the Personalized
C-Y Wu1, K Yu, S E Arnold
1Chao-Yi Wu, 149 13th floor, 10-003C, Boston, MA, USA, chwu3@mgh.harvard.edu, 02129; 617-724-2428.
The Journal of Prevention of Alzheimer'S Disease
|May 6, 2024
Summary
Individualized treatment response analysis reveals key factors influencing Alzheimer's Disease intervention outcomes. This approach helps identify responders and estimate potential delays in cognitive decline for better clinical trial design.
Area of Science:
- Neuroscience
- Gerontology
- Clinical Psychology
Background:
- Alzheimer's Disease (AD) clinical trials often fail due to disease heterogeneity.
- Individual traits and disease processes vary, impacting treatment efficacy.
- A novel approach is needed to understand differential treatment effects.
Purpose of the Study:
- To employ an individual-level treatment response (ITR) approach to identify characteristics of treatment responders.
- To estimate the time saved in cognitive decline using the I-CONECT behavioral intervention.
- To model heterogeneity in treatment effects for Alzheimer's Disease interventions.
Main Methods:
- Utilized data from the Internet-based Conversational Engagement Clinical Trial (I-CONECT).
- Generated ITR scores for global cognition (MoCA) and semantic fluency (CFA) using random forest models.
- Employed SHAP and decision trees to identify responder traits and estimated time saved.
Main Results:
- Substantial heterogeneity in treatment response observed for MoCA, modest for CFA.
- Age, cognitive status, social engagement, education, and personality influenced treatment response.
- Responders showed potential delays of 3-6 months in cognitive decline.
Conclusions:
- ITR analyses are valuable for profiling treatment responders and informing future trial design.
- Measuring time saved in cognitive decline offers insights into clinical meaningfulness.
- This approach can enhance personalized medicine for Alzheimer's Disease.
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