New Statistical Learning Methods for Personalized Medical Decision Making

This research focuses on developing new and computationally efficient statistical learning methods for multicategory classification and personalized medical decision making. Motivated by the challenge of multicategory classification problems, and the computational efficiency and theoretical properties of support vector machines (SVM), a novel learning algorithm is proposed. The method is then adapted to estimating multicategory individualized treatment by connecting with outcome weighted learning. At last, an application to Electronic Health Record data is explored. The proposed algorithm, forward-backward SVM (FB-SVM) is based on a sequential binary classification algorithm and relies on support vector machines for each binary classification and utilizes only feasible data in each step. The method guarantees convergence and entails light computational burden. We prove the theoretical property of Fisher consistency of the classification rule derived from the FB-SVM and obtain the risk bound for the predicted misclassification rate. We conduct extensive simulation and application studies, using popular benchmarking data and data from a newly completed real-world study, to demonstrate that the proposed method has superior performance, in terms of low misclassification rates and significantly improved computational speed when compared to existing methods. Furthermore, we generalize the proposed FB-SVM with outcome weighted learning to estimate optimal individualized treatment rule (ITR) with multiple options of treatment, namely sequential outcome-weighted learning (SOM). Theoretically, we show that the resulting ITR is Fisher consistent. We demonstrate the performance of proposed method with extensive simulations. An application to a three-arm randomized trial of treating major depressive disorder shows that an individualized treatment strategy tailored to individual characteristics such as patients' expectancy of treatment efficacy and baseline depression severity reduces depressive symptoms more than non-personalized treatment strategies. Finally, we discuss how the proposed SOM learning can be used to estimate optimal ITRs with safety concerns in high dimensional data with patients' adverse reaction records who have taken statin medicine. We adopt sampling techniques, inverse probability weighting, propensity score adjustment, and variable clustering along with SOM learning in our analysis. Considering patients' demographics and medical history, we are able to recommend the best statin drug which has the lowest risk to cause myopathy or rhabdomyolysis.Doctor of Philosoph

Optimal individualized decision rules from a multi-arm trial: A comparison of methods and an application to tailoring inter-donation intervals among blood donors in the UK.

There is a growing interest in precision medicine where individual heterogeneity is incorporated into decision-making and treatments are tailored to individuals to provide better healthcare. One important aspect of precision medicine is the estimation of the optimal individualized treatment rule (ITR) that optimizes the expected outcome. Most methods developed for this purpose are restricted to the setting with two treatments, while clinical studies with more than two treatments are common in practice. In this work, we summarize methods to estimate the optimal ITR in the multi-arm setting and compare their performance in large-scale clinical trials via simulation studies. We then illustrate their utilities with a case study using the data from the INTERVAL trial, which randomly assigned over 20,000 male blood donors from England to one of the three inter-donation intervals (12-week, 10-week, and eight-week) over two years. We estimate the optimal individualized donation strategies under three different objectives. Our findings are fairly consistent across five different approaches that are applied: when we target the maximization of the total units of blood collected, almost all donors are assigned to the eight-week inter-donation interval, whereas if we aim at minimizing the low hemoglobin deferral rates, almost all donors are assigned to donate every 12 weeks. However, when the goal is to maximize the utility score that "discounts" the total units of blood collected by the incidences of low hemoglobin deferrals, we observe some heterogeneity in the optimal inter-donation interval across donors and the optimal donor assignment strategy is highly dependent on the trade-off parameter in the utility function.Medical Research Council programme MC_UU_00002/

Active Clinical Trials for Personalized Medicine

Individualized treatment rules (ITRs) tailor treatments according to individual patient characteristics. They can significantly improve patient care and are thus becoming increasingly popular. The data collected during randomized clinical trials are often used to estimate the optimal ITRs. However, these trials are generally expensive to run, and, moreover, they are not designed to efficiently estimate ITRs. In this paper, we propose a cost-effective estimation method from an active learning perspective. In particular, our method recruits only the "most informative" patients (in terms of learning the optimal ITRs) from an ongoing clinical trial. Simulation studies and real-data examples show that our active clinical trial method significantly improves on competing methods. We derive risk bounds and show that they support these observed empirical advantages.Comment: 48 Page, 9 Figures. To Appear in JASA--T&

Subgroup Identification Using the personalized Package

A plethora of disparate statistical methods have been proposed for subgroup identification to help tailor treatment decisions for patients. However a majority of them do not have corresponding R packages and the few that do pertain to particular statistical methods or provide little means of evaluating whether meaningful subgroups have been found. Recently, the work of Chen, Tian, Cai, and Yu (2017) unified many of these subgroup identification methods into one general, consistent framework. The goal of the personalized package is to provide a corresponding unified software framework for subgroup identification analyses that provides not only estimation of subgroups, but evaluation of treatment effects within estimated subgroups. The personalized package allows for a variety of subgroup identification methods for many types of outcomes commonly encountered in medical settings. The package is built to incorporate the entire subgroup identification analysis pipeline including propensity score diagnostics, subgroup estimation, analysis of the treatment effects within subgroups, and evaluation of identified subgroups. In this framework, different methods can be accessed with little change in the analysis code. Similarly, new methods can easily be incorporated into the package. Besides familiar statistical models, the package also allows flexible machine learning tools to be leveraged in subgroup identification. Further estimation improvements can be obtained via efficiency augmentation