The Harvard-Yenching Institute: The Past and the Present

Introduction of Major Institution

Treatment Effect Estimation and Therapeutic Optimization Using Observational Data

Indiana University-Purdue University Indianapolis (IUPUI)In this dissertation, I address two essential questions of modern therapeutics: (1) to quantify the e ects of pharmacological agents as functions of patient's clinical characteristics; (2) to optimize individual treatment regimen in the presence of multiple treatment options. To address the rst question, I proposed a uni ed framework for the estimation of heterogeneous treatment e ect (x), which is expressed as a function of the patient characteristics x. The proposed framework not only covers most of the existing advantage-learning methods in the literature, but also enhances the robustness of di erent learning methods against outliers by allowing the selection of appropriate loss functions. To cope with high-dimensionality in x, I incorporated into the method modern machine learning algorithms including random forests, gradient boosting machines, and neural networks, for a more scalable implementation. To facilitate the wider use of the developed methods, I developed an R package RCATE, which is now posted on Github for public access. For therapeutic optimization, I developed a treatment recommendation system using o ine reinforcement learning. O ine reinforcement learning is a type of machine learning method that enables an agent to learn an optimal policy in the absence of an interactive environment, such as those encountered in the analysis of therapeutics data. The recommendation system optimizes long-term reward, while accounting for the safety of treatment regimens. I tested the method using data from the Systolic Blood Pressure Trial (SPRINT), which included multiple years of follow-up data from thousands of patients on many di erent antihypertensive drugs. Using the SPRINT data, I developed a treatment recommendation system for antihypertensive therapies

Robust estimation of heterogeneous treatment effects using electronic health record data

Estimation of heterogeneous treatment effects is an essential component of precision medicine. Model and algorithm-based methods have been developed within the causal inference framework to achieve valid estimation and inference. Existing methods such as the A-learner, R-learner, modified covariates method (with and without efficiency augmentation), inverse propensity score weighting, and augmented inverse propensity score weighting have been proposed mostly under the square error loss function. The performance of these methods in the presence of data irregularity and high dimensionality, such as that encountered in electronic health record (EHR) data analysis, has been less studied. In this research, we describe a general formulation that unifies many of the existing learners through a common score function. The new formulation allows the incorporation of least absolute deviation (LAD) regression and dimension reduction techniques to counter the challenges in EHR data analysis. We show that under a set of mild regularity conditions, the resultant estimator has an asymptotic normal distribution. Within this framework, we proposed two specific estimators for EHR analysis based on weighted LAD with penalties for sparsity and smoothness simultaneously. Our simulation studies show that the proposed methods are more robust to outliers under various circumstances. We use these methods to assess the blood pressure-lowering effects of two commonly used antihypertensive therapies

Whole genome sequencing of foodborne pathogens and global data sharing development

With the rapid development of molecular typing techniques for monitoring foodborne pathogens and outbreak investigations, whole genome sequencing (WGS) is gradually revealing its importance. In the context of the globalization of food trade, it’s urgent to establish details of the links between foodborne pathogens and human exposure in order to accurately monitor and reduce their occurrence. The accuracy of WGS is significantly better than prior analysis tools in the aspect. In this paper, we take Listeria monocytogenes as example to expound the monitoring of foodborne pathogens and the investigation of infection outbreaks, emphasizing the value of WGS in trace-back of foodborne diseases. The technologies for data generation and analysis are summarized, the practical application progress of WGS in the worldwide foodborne pathogen typing is emphasized, and the challenges in the future are prospected

Robust estimation of heterogeneous treatment effects: an algorithm-based approach

Heterogeneous treatment effect estimation is an essential element in the practice of tailoring treatment to suit the characteristics of individual patients. Most existing methods are not sufficiently robust against data irregularities. To enhance the robustness of the existing methods, we recently put forward a general estimating equation that unifies many existing learners. But the performance of model-based learners depends heavily on the correctness of the underlying treatment effect model. This paper addresses this vulnerability by converting the treatment effect estimation to a weighted supervised learning problem. We combine the general estimating equation with supervised learning algorithms, such as the gradient boosting machine, random forest, and artificial neural network, with appropriate modifications. This extension retains the estimators’ robustness while enhancing their flexibility and scalability. Simulation shows that the algorithm-based estimation methods outperform their model-based counterparts in the presence of nonlinearity and non-additivity. We developed an R package, RCATE, for public access to the proposed methods. To illustrate the methods, we present a real data example to compare the blood pressure-lowering effects of two classes of antihypertensive agents

Acceptance and commitment therapy for symptom interference in metastatic breast cancer patients: a pilot randomized trial

PURPOSE: Breast cancer is the leading cause of cancer mortality in women worldwide. With medical advances, metastatic breast cancer (MBC) patients often live for years with many symptoms that interfere with activities. However, there is a paucity of efficacious interventions to address symptom-related suffering and functional interference. Thus, this study examined the feasibility and preliminary efficacy of telephone-based acceptance and commitment therapy (ACT) for symptom interference with functioning in MBC patients. METHODS: Symptomatic MBC patients (N = 47) were randomly assigned to six telephone sessions of ACT or six telephone sessions of education/support. Patients completed measures of symptom interference and measures assessing the severity of pain, fatigue, sleep disturbance, depressive symptoms, and anxiety. RESULTS: The eligibility screening rate (64%) and high retention (83% at 8 weeks post-baseline) demonstrated feasibility. When examining within-group change, ACT participants showed decreases in symptom interference (i.e., fatigue interference and sleep-related impairment; Cohen's d range = - 0.23 to - 0.31) at 8 and 12 weeks post-baseline, whereas education/support participants showed minimal change in these outcomes (d range = - 0.03 to 0.07). Additionally, at 12 weeks post-baseline, ACT participants showed moderate decreases in fatigue and sleep disturbance (both ds = - 0.43), whereas education/support participants showed small decreases in these outcomes (ds = - 0.24 and - 0.18 for fatigue and sleep disturbance, respectively). Both the ACT and education/support groups showed reductions in depressive symptoms (ds = - 0.27 and - 0.28) at 12 weeks post-baseline. Group differences in all outcomes were not statistically significant. CONCLUSIONS: ACT shows feasibility and promise in improving fatigue and sleep-related outcomes in MBC patients and warrants further investigation

Causal inference methods for combining randomized trials and observational studies: a review

With increasing data availability, causal treatment effects can be evaluated across different datasets, both randomized controlled trials (RCTs) and observational studies. RCTs isolate the effect of the treatment from that of unwanted (confounding) co-occurring effects. But they may struggle with inclusion biases, and thus lack external validity. On the other hand, large observational samples are often more representative of the target population but can conflate confounding effects with the treatment of interest. In this paper, we review the growing literature on methods for causal inference on combined RCTs and observational studies, striving for the best of both worlds. We first discuss identification and estimation methods that improve generalizability of RCTs using the representativeness of observational data. Classical estimators include weighting, difference between conditional outcome models, and doubly robust estimators. We then discuss methods that combine RCTs and observational data to improve (conditional) average treatment effect estimation, handling possible unmeasured confounding in the observational data. We also connect and contrast works developed in both the potential outcomes framework and the structural causal model framework. Finally, we compare the main methods using a simulation study and real world data to analyze the effect of tranexamic acid on the mortality rate in major trauma patients. Code to implement many of the methods is provided