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作者：Chen, Yuan; Zeng, Donglin; Wang, Yuanjia
作者单位：Columbia University; University of North Carolina; University of North Carolina Chapel Hill
摘要：For many mental disorders, latent mental status from multiple-domain psychological or clinical symptoms may perform as a better characterization of the underlying disorder status than a simple summary score of the symptoms, and they may also serve as more reliable and representative features to differentiate treatment responses. Therefore, to address the complexity and heterogeneity of treatment responses for mental disorders, we provide a new paradigm for learning optimal individualized treat...
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作者：Chen, Ting-Huei; Chatterjee, Nilanjan; Landi, Maria Teresa; Shi, Jianxin
作者单位：Laval University; Johns Hopkins University; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics
摘要：Large-scale genome-wide association studies (GWAS) provide opportunities for developing genetic risk prediction models that have the potential to improve disease prevention, intervention or treatment. The key step is to develop polygenic risk score (PRS) models with high predictive performance for a given disease, which typically requires a large training dataset for selecting truly associated single nucleotide polymorphisms (SNPs) and estimating effect sizes accurately. Here, we develop a com...
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作者：Huling, Jared D.; Smith, Maureen A.; Chen, Guanhua
作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
摘要：Health care payments are an important component of health care utilization and are thus a major focus in health services and health policy applications. However, payment outcomes are semicontinuous in that over a given period of time some patients incur no payments and some patients incur large costs. Individualized treatment rules (ITRs) are a major part of the push for tailoring treatments and interventions to patients, yet there is a little work focused on estimating ITRs from semicontinuou...
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作者：Cui, Yifan; Tchetgen Tchetgen, Eric
作者单位：University of Pennsylvania
摘要：There is a fast-growing literature on estimating optimal treatment regimes based on randomized trials or observational studies under a key identifying condition of no unmeasured confounding. Because confounding by unmeasured factors cannot generally be ruled out with certainty in observational studies or randomized trials subject to noncompliance, we propose a general instrumental variable (IV) approach to learning optimal treatment regimes under endogeneity. Specifically, we establish identif...
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作者：Li, Xinran; Meng, Xiao-Li
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Harvard University
摘要：Transitional inference is an empiricism concept, rooted and practiced in clinical medicine since ancient Greece. Knowledge and experiences gained from treating one entity (e.g., a disease or a group of patients) are applied to treat a related but distinctively different one (e.g., a similar disease or a new patient). This notion of transition to the similar renders individualized treatments an operational meaning, yet its theoretical foundation defies the familiar inductive inference framework...
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作者：Lin, Ruitao; Thall, Peter F.; Yuan, Ying
作者单位：University of Texas System; UTMD Anderson Cancer Center
摘要：A Bayesian group sequential design is proposed that performs survival comparisons within patient subgroups in randomized trials where treatment-subgroup interactions may be present. A latent subgroup membership variable is assumed to allow the design to adaptively combine homogeneous subgroups, or split heterogeneous subgroups, to improve the procedure's within-subgroup power. If a baseline covariate related to survival is available, the design may incorporate this information to improve subgr...
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作者：Yadlowsky, Steve; Pellegrini, Fabio; Lionetto, Federica; Braune, Stefan; Tian, Lu
作者单位：Stanford University; Stanford University
摘要：While sample sizes in randomized clinical trials are large enough to estimate the average treatment effect well, they are often insufficient for estimation of treatment-covariate interactions critical to studying data-driven precision medicine. Observational data from real world practice may play an important role in alleviating this problem. One common approach in trials is to predict the outcome of interest with separate regression models in each treatment arm, and estimate the treatment eff...
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作者：Nie, Xinkun; Brunskill, Emma; Wager, Stefan
作者单位：Stanford University; Stanford University
摘要：Many applied decision-making problems have a dynamic component: The policymaker needs not only to choose whom to treat, but also when to start which treatment. For example, a medical doctor may choose between postponing treatment (watchful waiting) and prescribing one of several available treatments during the many visits from a patient. We develop an advantage doubly robust estimator for learning such dynamic treatment rules using observational data under the assumption of sequential ignorabi...
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作者：Sun, Yilun; Wang, Lu
作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
摘要：A dynamic treatment regime (DTR) is a sequence of decision rules that adapt to the time-varying states of an individual. Black-box learning methods have shown great potential in predicting the optimal treatments; however, the resulting DTRs lack interpretability, which is of paramount importance for medical experts to understand and implement. We present a stochastic tree-based reinforcement learning (ST-RL) method for estimating optimal DTRs in a multistage multitreatment setting with data fr...
