Masters in Statistics Thesis Defense: Subgroup Identification via Interaction Tree and Mixed Model for Repeated Measures with Application to Alzheimer’s Disease

Brief abstract: Alzheimer's disease (AD) is a progressive, degenerative disorder of the brain and is the most common form of dementia in the aging population. Significant heterogeneity exists among individuals with Alzheimer's disease (Davidson et al. 2010, Duits et al. 2021, Liu et al. 2022), which may lead to varying responses to medications. Consequently, traditional one-size-fits-all treatments for Alzheimer's disease may be inadequate, giving rise to the need for personalized treatment plans. In order to enhance the safety, efficacy, and efficiency of Alzheimer's treatment medications, it is crucial to identify and treat patients who are most likely to respond positively to a specific medication. We present the Interaction Tree with Mixed Model for Repeated Measures (IT-MMRM) for subgroup identification, which combines the interaction tree approach with the Mixed Model for Repeated Measures (MMRM) method. Through simulation experiments, we demonstrate that the IT-MMRM outperforms its competitors in subgroup identification and provides guidance for selecting hyperparameters to optimize its performance. We applied the IT-MMRM algorithm to the "Vitamin E and Donepezil for the Treatment of Mild Cognitive Impairment" clinical trial dataset. Our method facilitated the detection of potential subgroups and offered direction for future investigations.