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作者：Hu, Xinyu; Qian, Min; Cheng, Bin; Cheung, Ying Kuen
作者单位：Columbia University
摘要：Personalized policy represents a paradigm shift one decision rule for all users to an individualized decision rule for each user. Developing personalized policy in mobile health applications imposes challenges. First, for lack of adherence, data from each user are limited. Second, unmeasured contextual factors can potentially impact on decision making. Aiming to optimize immediate rewards, we propose using a generalized linear mixed modeling framework where population features and individual f...
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作者：Bopp, Gregory P.; Shaby, Benjamin A.; Huser, Raphael
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Colorado State University System; Colorado State University Fort Collins; King Abdullah University of Science & Technology
摘要：Understanding the spatial extent of extreme precipitation is necessary for determining flood risk and adequately designing infrastructure (e.g., stormwater pipes) to withstand such hazards. While environmental phenomena typically exhibit weakening spatial dependence at increasingly extreme levels, limiting max-stable process models for block maxima have a rigid dependence structure that does not capture this type of behavior. We propose a flexible Bayesian model from a broader family of (condi...
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作者：Zhang, Bo; Weiss, Jordan; Small, Dylan S.; Zhao, Qingyuan
作者单位：University of Pennsylvania; University of Pennsylvania; University of Cambridge
摘要：It is common to compare individualized treatment rules based on the value function, which is the expected potential outcome under the treatment rule. Although the value function is not point-identified when there is unmeasured confounding, it still defines a partial order among the treatment rules under Rosenbaum's sensitivity analysis model. We first consider how to compare two treatment rules with unmeasured confounding in the single-decision setting and then use this pairwise test to rank m...
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作者：Ben Taieb, Souhaib; Taylor, James W.; Hyndman, Rob J.
作者单位：University of Mons; University of Oxford; Monash University
摘要：Decisions regarding the supply of electricity across a power grid must take into consideration the inherent uncertainty in demand. Optimal decision-making requires probabilistic forecasts for demand in a hierarchy with various levels of aggregation, such as substations, cities, and regions. The forecasts should be coherent in the sense that the forecast of the aggregated series should equal the sum of the forecasts of the corresponding disaggregated series. Coherency is essential, since the al...
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作者：Kosorok, Michael R.; Laber, Eric B.; Small, Dylan S.; Zeng, Donglin
作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; North Carolina State University; University of Pennsylvania
摘要：We introduce the Theory and Methods Special Issue on Precision Medicine and Individualized Policy Discovery. The issue consists of four discussion papers, grouped into two pairs, and sixteen regular research papers that cover many important lines of research on data-driven decision making. We hope that the many provocative and original ideas presented herein will inspire further work and development in precision medicine and personalization.
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作者：Chiu, Grace S.
作者单位：William & Mary; Virginia Institute of Marine Science
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作者：Park, Seyoung; Xu, Hao; Zhao, Hongyu
作者单位：Sungkyunkwan University (SKKU); Yale University
摘要：Advances in high-throughput genomic technologies coupled with large-scale studies including The Cancer Genome Atlas (TCGA) project have generated rich resources of diverse types of omics data to better understand cancer etiology and treatment responses. Clustering patients into subtypes with similar disease etiologies and/or treatment responses using multiple omics data types has the potential to improve the precision of clustering than using a single data type. However, in practice, patient c...
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作者：Pan, Yinghao; Zhao, Ying-Qi
作者单位：University of North Carolina; University of North Carolina Charlotte; Fred Hutchinson Cancer Center
摘要：Individualized treatment rules (ITRs) recommend treatment according to patient characteristics. There is a growing interest in developing novel and efficient statistical methods in constructing ITRs. We propose an improved doubly robust estimator of the optimal ITRs. The proposed estimator is based on a direct optimization of an augmented inverse-probability weighted estimator of the expected clinical outcome over a class of ITRs. The method enjoys two key properties. First, it is doubly robus...
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作者：Qiu, Hongxiang; Carone, Marco; Sadikova, Ekaterina; Petukhova, Maria; Kessler, Ronald C.; Luedtke, Alex
作者单位：University of Washington; University of Washington Seattle; Harvard University; Harvard Medical School; University of Washington; University of Washington Seattle
摘要：There is an extensive literature on the estimation and evaluation of optimal individualized treatment rules in settings where all confounders of the effect of treatment on outcome are observed. We study the development of individualized decision rules in settings where some of these confounders may not have been measured but a valid binary instrument is available for a binary treatment. We first consider individualized treatment rules, which will naturally be most interesting in settings where...
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作者：Qiu, Hongxiang; Carone, Marco; Sadikova, Ekaterina; Petukhova, Maria; Kessler, Ronald C.; Luedtke, Alex
作者单位：University of Washington; University of Washington Seattle; Harvard University; Harvard Medical School; University of Washington; University of Washington Seattle
