New Advances in Personalized Medicine with Innovative Applications

Individualized treatment rule (ITR), which tailors treatments to individual patient's characteristics,
can be estimated using data from a randomized clinical trial (RCT). However, RCT is often conducted
under restricted inclusion criteria, so they are not representative of a target population. As the result, the ITR benefit
shown in the trial may not accurately reflect what will be attained in the target population. In this work, using a
large feature data from the target population, we first evaluate the benefit of ITRs in the target population, and then
we propose a new estimate for ITR based on two derived features which essentially correspond to a predictive score
and a residual score. The new ITR estimator leads to the smallest variance of the benefit in the target population.
Finally, we demonstrate the performance of the proposed method through simulation studies and an application to
treating type 2 diabetes. 

Off-Policy Evaluation (OPE) aims to estimate the value of a target policy using offline data collected from a potentially different policy. In this talk, we consider OPE in infinite horizon and irregular observation settings where the number of decision points diverges to infinity and the occurrence times of decision points are not regular and could be informative. We develop a new framework for OPE in a relatively general setting, and construct confidence intervals for a given policy's value with reinforcement learning techniques. Since the irregular decision making times could be informative, we also develop a Cox-type renewal process model on the occurrence times of the decision points, and based on the model, we develop an adaptive estimation procedure which leads to more efficient estimates for the policy value. Simulation studies and a real data application are conducted to illustrate the proposed methods.
 

Mental disorders present challenges in diagnosis and treatment due to their complex and heterogeneous nature. EEG has shown promise as a potential biomarker for these disorders. However, existing methods for analyzing EEG signals have limitations in addressing heterogeneity and capturing complex brain activity patterns between regions. We proposes a novel random effects state-space model (RESSM) for analyzing multi-channel resting-state EEG signals, accounting for the heterogeneity of brain connectivities between groups and individual subjects. We incorporate multi-level random effects for temporal dynamical and spatial mapping matrices and address nonstationarity so that the brain connectivity patterns can vary over time. The model is fitted under a Bayesian hierarchical model framework coupled with a Gibbs sampler. Through extensive simulation studies, we demonstrate that our approach yields valid estimation and inference. We apply RESSM to a multi-site clinical trial of Major Depressive Disorder (MDD). Our analysis uncovers significant differences in resting-state brain temporal dynamics among MDD patients compared to healthy individuals.