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Statistical and Machine Learning Methods to Improve Dynamic Treatment Regimens Estimation Using Real World Data

使用真实世界数据改进动态治疗方案估计的统计和机器学习方法

基本信息

批准号：
10654927
负责人：
Yuanjia Wang
金额：
--
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-04-01 至 2023-08-27
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10654927
关键词：
Academic Medical Centers Acceleration Achievement Address Adherence Adverse event Affect Algorithms American Atherosclerosis Benefits and Risks Center for Translational Science Activities Chronic Kidney Failure Clinical Trials Complement Computer software Data Data Set Databases Decision Making Diabetes Mellitus Dimensions Disadvantaged Disease Effectiveness Electronic Health Record Ensure Epidemic Equilibrium Exclusion Feedback Funding Goals Guidelines Health Status Hypoglycemia Individual Inequality Joints Lead Learning Length Maintenance Managed Care Medical Medical center Methods Modeling Non-Insulin-Dependent Diabetes Mellitus Obesity Observational Study Ohio Outcome Patient Care Patient Preferences Patients Pattern Performance Population Population Distributions Population Heterogeneity Procedures Process Public Health Quality Control Randomized, Controlled Trials Recommendation Risk Safety Sample Size Sampling Selection Bias Statistical Methods Surveys Time Treatment Protocols Uncertainty Universities Update algorithm development cardiovascular disorder risk clinical decision-making clinical practice cohort comorbidity cost efficient data modeling disease registry efficacy outcomes feature selection improved individual patient individualized medicine insight knowledge base learning algorithm learning strategy machine learning algorithm machine learning method method development novel optimal treatments patient oriented patient population personalized care personalized decision precision medicine preference prospective semiparametric side effect simulation social health determinants software development statistical and machine learning statistical learning theories tool translational clinical trial treatment choice treatment effect treatment guidelines treatment pattern treatment strategy

项目摘要

Project Summary/Abstract Type 2 diabetes (T2D) is a global epidemic affecting approximately 462 million individuals world-wide. Cur- rent medical treatment guidelines rely largely on data from randomized controlled trials (RCTs) that study average effects, which is far from adequate for making individualized decisions for real world patients. This limitation is even worse for discovering dynamic treatment regimens (DTRs) in a heterogeneous population where treatment decisions are made over one or more stages of disease course. This limitation can be partially addressed by sup- plementing RCT data with real world data (RWD), such as disease registries, prospective observational studies, surveys and electronic health records, to improve medical decision making. Despite of the promise of combining RWD and RCT, there are several signiﬁcant challenges in method and algorithm development. These include lack of generalizability or practical utility for the ﬁndings from RCTs when applied to real world patients; bias due to unobserved confounders; and concern about long-term side effects/risks. This proposal aims to address each of these challenges. Speciﬁcally, in Aim 1, we address the generalizability issue by proposing a novel framework that uses evidence from RWD to improve learning DTRs in the trials. The framework uses RWD to select infor- mative tailoring features, balance population distributions and improve statistical efﬁciency through doubly robust estimation. In Aim 2, to improve the practical utility of DTRs, we propose a robust method to ﬁrst infer individual treatment choice/preference from RWD, then incorporate this estimated preference into learning DTRs using the trial data. The resulting DTRs are not only statistically valid but also compatible with patient/clinician preference in real world populations. In Aim 3, to lessen the bias due to hidden confounders in RWD, we propose joint semiparametric models to combine the trial data with RWD; the models we propose allow different magnitudes of treatment effect sizes and control for possible bias due to hidden confounders in RWD. In Aim 4, to address the concern about long-term risks, we consider a general procedure for estimating DTRs that maximizes efﬁcacy outcomes while ensuring that long-term side effects associated with the recommended DTRs remain below a certain threshold. We then propose a novel simultaneous learning algorithm to estimate the optimal DTRs across all stages. For all four aims, we will provide rigorous assumptions and theoretical justiﬁcations using tools from concentration inequalities, statistical learning theory, empirical processes and semiparametric inference. We will conduct extensive simulation studies to study the performance of the proposed approaches in a variety of set- tings, and compare their performance with off-the-shelf methods. We will apply the proposed methods to estimate DTRs for T2D using clinical trial data and RWD taken from electronic health records in Columbia University and Ohio State University medical centers as well as Allof Us precision medicine study. Our methods and ﬁndings will be publicized through software development; the software will receive frequent updates based on user feedback.

项目总结/文摘