Background Machine learning–based approaches that seek to accomplish individualized
treatment effect prediction have gained traction; however, some salient challenges lack …

Objective: To apply the latest guidance for estimating and evaluating heterogeneous
treatment effects (HTEs) in an end-to-end case study of the Long-term Anticoagulation …

There is increasing interest in moving away from “one size fits all (OSFA)” approaches
toward stratifying treatment decisions. Understanding how expected effectiveness and cost …

Studies involving both randomized experiments as well as observational data typically
involve time-to-event outcomes such as time-to-failure, death or onset of an adverse …

An individualized treatment regime (ITR) is a decision rule that assigns treatments based on
patients' characteristics. The value function of an ITR is the expected outcome in a …

Methods for estimating heterogeneous treatment effects (HTE) from observational data have
largely focused on continuous or binary outcomes, with less attention paid to survival …

Non-significant randomized control trials can hide subgroups of good responders to
experimental drugs, thus hindering subsequent development. Identifying such …

In this dissertation, we propose novel Deep Neural Network (DNN) based statistical learning
models that can provide accurate predictions and clear interpretations simultaneously …

Abstract Time-to-Event Regression, often referred to as Survival Analysis or Censored
Regression involves learning of statistical estimators of the survival distribution of an …

The availability of large observational datasets in healthcare presents an opportunity to
leverage machine learning techniques to learn complex relationships between an …