Journal of Data Science

Modern precision medicine aims to utilize real-world data to provide the best treatment for an individual patient. An individualized treatment rule (ITR) maps each patient’s characteristics to a recommended treatment scheme that maximizes the expected outcome of the patient. A challenge precision medicine faces is population heterogeneity, as studies on treatment effects are often conducted on source populations that differ from the populations of interest in terms of the distribution of patient characteristics. Our research goal is to explore a transfer learning algorithm that aims to address the population heterogeneity problem and obtain targeted, optimal, and interpretable ITRs. The algorithm incorporates a calibrated augmented inverse probability weighting estimator for the average treatment effect and employs value function maximization for the target population using Genetic Algorithm to produce our desired ITR. To demonstrate its practical utility, we apply this transfer learning algorithm to two large medical databases, eICU Collaborative Research Database and Medical Information Mart for Intensive Care III. We first identify the important covariates, treatment options, and outcomes of interest based on the two databases, and then estimate the optimal linear ITRs for patients with sepsis. Our research introduces and applies new techniques for data fusion to obtain data-driven ITRs that cater to patients’ individual medical needs in a population of interest. By emphasizing generalizability and personalized decision-making, this methodology extends its potential application beyond medicine to fields such as marketing, technology, social sciences, and education.

One crucial aspect of precision medicine is to allow physicians to recommend the most suitable treatment for their patients. This requires understanding the treatment heterogeneity from a patient-centric view, quantified by estimating the individualized treatment effect (ITE). With a large amount of genetics data and medical factors being collected, a complete picture of individuals’ characteristics is forming, which provides more opportunities to accurately estimate ITE. Recent development using machine learning methods within the counterfactual outcome framework shows excellent potential in analyzing such data. In this research, we propose to extend meta-learning approaches to estimate individualized treatment effects with survival outcomes. Two meta-learning algorithms are considered, T-learner and X-learner, each combined with three types of machine learning methods: random survival forest, Bayesian accelerated failure time model and survival neural network. We examine the performance of the proposed methods and provide practical guidelines for their application in randomized clinical trials (RCTs). Moreover, we propose to use the Boruta algorithm to identify risk factors that contribute to treatment heterogeneity based on ITE estimates. The finite sample performances of these methods are compared through extensive simulations under different randomization designs. The proposed approach is applied to a large RCT of eye disease, namely, age-related macular degeneration (AMD), to estimate the ITE on delaying time-to-AMD progression and to make individualized treatment recommendations.