Characterizing Treatment Non-responders and Responders in Completed Alzheimer's Disease Clinical Trials.

Characterizing differential responses to Alzheimer's disease (AD) drugs will provide better insights into personalized treatment strategies. Our study aims to identify heterogeneous treatment effects and pre-treatment features that moderate the treatment effect of Galantamine, Bapineuzumab, and Semagacestat from completed trial data. The causal forest method can capture heterogeneity in treatment responses. We applied causal forest modeling to estimate the treatment effect and identify efficacy moderators in each trial. We found several patient's pretreatment conditions that determined treatment efficacy. For example, in Galantamine trials, whole brain volume (1092.54 vs. 1060.67 ml, P < .001) and right hippocampal volume (2.43e-3 vs. 2.79e-3, P < .001) are significantly different between responsive and non-responsive subgroups. Overall, our implementation of causal forests in AD clinical trials reveals the heterogeneous treatment effects and different moderators for AD drug responses, highlighting promising personalized treatment based on patient-specific characteristics in AD research and drug development.