Innovative precision medicine methods in subgroup identification for Alzheimer's disease

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is a major and rapidly increasing public health concern: over 30 million individuals worldwide suffer from AD, which is projected to quadruple by 2050. AD has been reported to be the third leading cause of death in the US. With this impending global public health crisis, treatments that prevent onset or slow progression of AD are urgently needed but rarely available until the recent accelerated approval for aducenumab. Therefore, it is of great interest to identify subpopulations which benefit most from a medication when the overall treatment effect is minimum or not clinically meaningful. If such subpopulations can be identified, some of the treatments from the negative trials can be proven to at least help a portion of the AD population. In this proposal we will employ non-parametric interaction tree (IT)-based methods on mixed models for repeated measures (MMRM) and regression-based methods to identify such subpopulations. IT for MMRM builds on the assessment of the treatment-by-covariates interactions and can automatically seek subgroups of individuals in whom the treatment shows heterogeneous effects. We also explore a new and more attractive fusion penalty approach for final tree determination without any prior knowledge of grouping information. The regression-based methods aim to identify subpopulations who will benefit from AD treatment based on their characteristics, which is very flexible to make individualized treatment selection. Finally, we will develop and disseminate a user-friendly statistical software package that will enable researchers to implement these methods with ease. Our extensions will better capture individual heterogeneity in disease progression and facilitate evidence-based precision medicine in future AD studies and other research areas.