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Sun W, Liu J, Hu J, Jin J, Siasoco K, Zhou R, Mccoy R. Adaptive restraint design for a diverse population through machine learning. Front Public Health 2023; 11:1202970. [PMID: 37637800 PMCID: PMC10448517 DOI: 10.3389/fpubh.2023.1202970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 07/27/2023] [Indexed: 08/29/2023] Open
Abstract
Objective Using population-based simulations and machine-learning algorithms to develop an adaptive restraint system that accounts for occupant anthropometry variations to further enhance safety balance throughout the whole population. Methods Two thousand MADYMO full frontal impact crash simulations at 35 mph using two validated vehicle/restraint models representing a sedan and an SUV along with a parametric occupant model were conducted based on the maximal projection design of experiments, which considers varying occupant covariates (sex, stature, and body mass index) and vehicle restraint design variables (three for airbag, three for safety belt, and one for knee bolster). A Gaussian-process-based surrogate model was trained to rapidly predict occupant injury risks and the associated uncertainties. An optimization framework was formulated to seek the optimal adaptive restraint design policy that minimizes the population injury risk across a wide range of occupant sizes and shapes while maintaining a low difference in injury risks among different occupant subgroups. The effectiveness of the proposed method was tested by comparing the population-wise injury risks under the adaptive design policy and the traditional state-of-the-art design. Results Compared to the traditional state-of-the-art design for midsize males, the optimal design policy shows the potential to further reduce the joint injury risk (combining head, chest, and lower extremity injury risks) among the whole population in the sedan and SUV models. Specifically, the two subgroups of vulnerable occupants including tall obese males and short obese females had higher reductions in injury risks. Conclusions This study lays out a method to adaptively adjust vehicle restraint systems to improve safety balance. This is the first study where population-based crash simulations and machine-learning methods are used to optimize adaptive restraint designs for a diverse population. Nevertheless, this study shows the high injury risks associated with obese and female occupants, which can be mitigated via restraint adaptability.
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Affiliation(s)
- Wenbo Sun
- University of Michigan Transportation Research Institute (UMTRI), College of Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Jiacheng Liu
- Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Jingwen Hu
- University of Michigan Transportation Research Institute (UMTRI), College of Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Judy Jin
- Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, United States
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2
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Li Z, Chen J, Laber E, Liu F, Baumgartner R. Optimal Treatment Regimes: A Review and Empirical Comparison. Int Stat Rev 2023. [DOI: 10.1111/insr.12536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Affiliation(s)
- Zhen Li
- Department of Statistics North Carolina State University Raleigh 27607 NC USA
| | - Jie Chen
- Department of Biometrics Overland Pharmaceuticals Dover 19901 DE USA
| | - Eric Laber
- Department of Statistical Science, Department of Biostatistics and Bioinformatics Duke University Durham 27708 NC USA
| | - Fang Liu
- Biostatistics and Research Decision Sciences Merck & Co., Inc. Kenilworth NJ 07033 USA
| | - Richard Baumgartner
- Biostatistics and Research Decision Sciences Merck & Co., Inc. Kenilworth NJ 07033 USA
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3
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Nearly Dimension-Independent Sparse Linear Bandit over Small Action Spaces via Best Subset Selection. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2108816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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4
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Shi C, Luo S, Le Y, Zhu H, Song R. Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite Horizons. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2106868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Affiliation(s)
| | | | - Yuan Le
- Shanghai University of Finance and Economics
| | - Hongtu Zhu
- University of North Carolina at Chapel Hill
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5
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Shiranthika C, Chen KW, Wang CY, Yang CY, Sudantha BH, Li WF. Supervised Optimal Chemotherapy Regimen Based on Offline Reinforcement Learning. IEEE J Biomed Health Inform 2022; 26:4763-4772. [PMID: 35714083 DOI: 10.1109/jbhi.2022.3183854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In recent years, reinforcement learning (RL) has achieved a remarkable achievement and it has attracted researchers' attention in modeling real-life scenarios by expanding its research beyond conventional complex games. Prediction of optimal treatment regimens from observational real clinical data is being popularized, and more advanced versions of RL algorithms are being implemented in the literature. However, RL-generated medications still need careful supervision of expertise parties or doctors in healthcare. Hence, in this paper, a Supervised Optimal Chemotherapy Regimen (SOCR) approach to investigate optimal chemotherapy-dosing schedule for cancer patients was presented by using Offline Reinforcement Learning. The optimal policy suggested by the RL approach was supervised by incorporating previous treatment decisions of oncologists, which could add clinical expertise knowledge on algorithmic results. Presented SOCR approach followed a model-based architecture using conservative Q-Learning (CQL) algorithm. The developed model was tested using a manually constructed database of forty Stage-IV colon cancer patients, receiving line-1 chemotherapy treatments, who were clinically classified as 'Bevacizumab based patient' and 'Cetuximab based patient'. Experimental results revealed that the supervision from the oncologists has considered the effect to stabilize chemotherapy regimen and it was suggested that the proposed framework could be successfully used as a supportive model for oncologists in deciding their treatment decisions.
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6
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Zhou Y, Wang L, Song R, Zhao T. Transformation-Invariant Learning of Optimal Individualized Decision Rules with Time-to-Event Outcomes. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2068420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Yu Zhou
- Roku, San Jose, United States
| | - Lan Wang
- Department of Management Science, University of Miami
| | - Rui Song
- Department of Statistics, North Carolina State University
| | - Tuoyi Zhao
- Department of Management Science, University of Miami
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7
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Shi C, Wang X, Luo S, Zhu H, Ye J, Song R. Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2027776] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
| | - Xiaoyu Wang
- Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences
| | | | - Hongtu Zhu
- The Univeristy of North Carolina at Chapell Hill
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8
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Fang EX, Wang Z, Wang L. Fairness-Oriented Learning for Optimal Individualized Treatment Rules. J Am Stat Assoc 2021. [DOI: 10.1080/01621459.2021.2008402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Ethan X. Fang
- Department of Statistics, Pennsylvania State University, University Park, PA 16802
| | - Zhaoran Wang
- Department of Industrial al Engineering and Management Science, Northwestern University, Evanston, IL 60208
| | - Lan Wang
- Department of Management Science, Miami Herbert Business School, University of Miami, Coral Gables, FL 33146
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9
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Survival Augmented Patient Preference Incorporated Reinforcement Learning to Evaluate Tailoring Variables for Personalized Healthcare. STATS 2021. [DOI: 10.3390/stats4040046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
In this paper, we consider personalized treatment decision strategies in the management of chronic diseases, such as chronic kidney disease, which typically consists of sequential and adaptive treatment decision making. We investigate a two-stage treatment setting with a survival outcome that could be right censored. This can be formulated through a dynamic treatment regime (DTR) framework, where the goal is to tailor treatment to each individual based on their own medical history in order to maximize a desirable health outcome. We develop a new method, Survival Augmented Patient Preference incorporated reinforcement Q-Learning (SAPP-Q-Learning) to decide between quality of life and survival restricted at maximal follow-up. Our method incorporates the latent patient preference into a weighted utility function that balances between quality of life and survival time, in a Q-learning model framework. We further propose a corresponding m-out-of-n Bootstrap procedure to accurately make statistical inferences and construct confidence intervals on the effects of tailoring variables, whose values can guide personalized treatment strategies.
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10
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Wu Y, Wang L, Fu H. Model-Assisted Uniformly Honest Inference for Optimal Treatment Regimes in High Dimension. J Am Stat Assoc 2021. [DOI: 10.1080/01621459.2021.1929246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Yunan Wu
- Yale University, Department of Biostatistics, New Haven, 06520 United States
| | - Lan Wang
- University of Miami, Department of Management Science, Coral Gables, 33124 United States
| | - Haoda Fu
- Eli Lilly and Company, Biometrics and Advanced Analytics, Indianapolis, United States
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11
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Hu X, Qian M, Cheng B, Cheung YK. Personalized Policy Learning using Longitudinal Mobile Health Data. J Am Stat Assoc 2020; 116:410-420. [PMID: 34239215 DOI: 10.1080/01621459.2020.1785476] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Personalized policy represents a paradigm shift from 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 features are modeled as fixed and random effects, respectively, and synthesized to form the personalized policy. The group lasso type penalty is imposed to avoid overfitting of individual deviations from the population model. We examine the conditions under which the proposed method work in the presence of time-varying endogenous covariates, and provide conditional optimality and marginal consistency results of the expected immediate outcome under the estimated policies. We apply our method to develop personalized push ("prompt") schedules in 294 app users, with the goal to maximize the prompt response rate given past app usage and other contextual factors. The proposed method compares favorably to existing estimation methods including using the R function "glmer" in a simulation study.
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Affiliation(s)
- Xinyu Hu
- Department of Biostatistics, Columbia University
| | - Min Qian
- Department of Biostatistics, Columbia University
| | - Bin Cheng
- Department of Biostatistics, Columbia University
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12
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Sessa M, Khan AR, Liang D, Andersen M, Kulahci M. Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 1-Overview of Knowledge Discovery Techniques in Artificial Intelligence. Front Pharmacol 2020; 11:1028. [PMID: 32765261 PMCID: PMC7378532 DOI: 10.3389/fphar.2020.01028] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 06/24/2020] [Indexed: 12/14/2022] Open
Abstract
Aim To perform a systematic review on the application of artificial intelligence (AI) based knowledge discovery techniques in pharmacoepidemiology. Study Eligibility Criteria Clinical trials, meta-analyses, narrative/systematic review, and observational studies using (or mentioning articles using) artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded. Data Sources Articles recorded from 1950/01/01 to 2019/05/06 in Ovid MEDLINE were screened. Participants Studies including humans (real or simulated) exposed to a drug. Results In total, 72 original articles and 5 reviews were identified via Ovid MEDLINE. Twenty different knowledge discovery methods were identified, mainly from the area of machine learning (66/72; 91.7%). Classification/regression (44/72; 61.1%), classification/regression + model optimization (13/72; 18.0%), and classification/regression + features selection (12/72; 16.7%) were the three most frequent tasks in reviewed literature that machine learning methods has been applied to solve. The top three used techniques were artificial neural networks, random forest, and support vector machines models. Conclusions The use of knowledge discovery techniques of artificial intelligence techniques has increased exponentially over the years covering numerous sub-topics of pharmacoepidemiology. Systematic Review Registration Systematic review registration number in PROSPERO: CRD42019136552.
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Affiliation(s)
- Maurizio Sessa
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Abdul Rauf Khan
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.,Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - David Liang
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Morten Andersen
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Murat Kulahci
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.,Department of Business Administration, Technology and Social Sciences, Luleå University of Technology, Luleå, Sweden
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13
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Abstract
Estimating optimal individualized treatment rules (ITRs) in single or multi-stage clinical trials is one key solution to personalized medicine and has received more and more attention in statistical community. Recent development suggests that using machine learning approaches can significantly improve the estimation over model-based methods. However, proper inference for the estimated ITRs has not been well established in machine learning based approaches. In this paper, we propose a entropy learning approach to estimate the optimal individualized treatment rules (ITRs). We obtain the asymptotic distributions for the estimated rules so further provide valid inference. The proposed approach is demonstrated to perform well in finite sample through extensive simulation studies. Finally, we analyze data from a multi-stage clinical trial for depression patients. Our results offer novel findings that are otherwise not revealed with existing approaches.
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Affiliation(s)
- Binyan Jiang
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.,
| | - Rui Song
- Department of Statistics, North Carolina State University, North Carolina 27695, USA.,
| | - Jialiang Li
- Department of Statistics and Applied Probability, National University of Singapore, 117546, Singapore.,
| | - Donglin Zeng
- Department of Statistics, North Carolina State University, North Carolina 27695, USA.,
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14
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Ghosh P, Nahum-Shani I, Spring B, Chakraborty B. Noninferiority and equivalence tests in sequential, multiple assignment, randomized trials (SMARTs). Psychol Methods 2019; 25:182-205. [PMID: 31497981 DOI: 10.1037/met0000232] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Adaptive interventions (AIs) are increasingly popular in the behavioral sciences. An AI is a sequence of decision rules that specify for whom and under what conditions different intervention options should be offered, in order to address the changing needs of individuals as they progress over time. The sequential, multiple assignment, randomized trial (SMART) is a novel trial design that was developed to aid in empirically constructing effective AIs. The sequential randomizations in a SMART often yield multiple AIs that are embedded in the trial by design. Many SMARTs are motivated by scientific questions pertaining to the comparison of such embedded AIs. Existing data analytic methods and sample size planning resources for SMARTs are suitable only for superiority testing, namely for testing whether one embedded AI yields better primary outcomes on average than another. This calls for noninferiority/equivalence testing methods, because AIs are often motivated by the need to deliver support/care in a less costly or less burdensome manner, while still yielding benefits that are equivalent or noninferior to those produced by a more costly/burdensome standard of care. Here, we develop data-analytic methods and sample-size formulas for SMARTs testing the noninferiority or equivalence of one AI over another. Sample size and power considerations are discussed with supporting simulations, and online resources for sample size planning are provided. A simulated data analysis shows how to test noninferiority and equivalence hypotheses with SMART data. For illustration, we use an example from a SMART in the area of health psychology aiming to develop an AI for promoting weight loss among overweight/obese adults. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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15
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Abstract
Precision medicine seeks to maximize the quality of healthcare by individualizing the healthcare process to the uniquely evolving health status of each patient. This endeavor spans a broad range of scientific areas including drug discovery, genetics/genomics, health communication, and causal inference all in support of evidence-based, i.e., data-driven, decision making. Precision medicine is formalized as a treatment regime which comprises a sequence of decision rules, one per decision point, which map up-to-date patient information to a recommended action. The potential actions could be the selection of which drug to use, the selection of dose, timing of administration, specific diet or exercise recommendation, or other aspects of treatment or care. Statistics research in precision medicine is broadly focused on methodological development for estimation of and inference for treatment regimes which maximize some cumulative clinical outcome. In this review, we provide an overview of this vibrant area of research and present important and emerging challenges.
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Affiliation(s)
- Michael R Kosorok
- Department of Biostatistics and Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, U.S.A.;
| | - Eric B Laber
- Department of Statistics, North Carolina State University, Raleight, North Carolina, 27695, U.S.A.;
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16
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Zhu W, Zeng D, Song R. Proper Inference for Value Function in High-Dimensional Q-Learning for Dynamic Treatment Regimes. J Am Stat Assoc 2018; 114:1404-1417. [PMID: 31929664 DOI: 10.1080/01621459.2018.1506341] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Dynamic treatment regimes are a set of decision rules and each treatment decision is tailored over time according to patients' responses to previous treatments as well as covariate history. There is a growing interest in development of correct statistical inference for optimal dynamic treatment regimes to handle the challenges of non-regularity problems in the presence of non-respondents who have zero-treatment effects, especially when the dimension of the tailoring variables is high. In this paper, we propose a high-dimensional Q-learning (HQ-learning) to facilitate the inference of optimal values and parameters. The proposed method allows us to simultaneously estimate the optimal dynamic treatment regimes and select the important variables that truly contribute to the individual reward. At the same time, hard thresholding is introduced in the method to eliminate the effects of the non-respondents. The asymptotic properties for the parameter estimators as well as the estimated optimal value function are then established by adjusting the bias due to thresholding. Both simulation studies and real data analysis demonstrate satisfactory performance for obtaining the proper inference for the value function for the optimal dynamic treatment regimes.
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Affiliation(s)
- Wensheng Zhu
- Key Laboratory for Applied Statistics of MOE,School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China
| | - Donglin Zeng
- Departments of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Rui Song
- Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA
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17
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Jeng XJ, Lu W, Peng H. High-Dimensional Inference for Personalized Treatment Decision. Electron J Stat 2018; 12:2074-2089. [PMID: 30416643 PMCID: PMC6226259 DOI: 10.1214/18-ejs1439] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Recent development in statistical methodology for personalized treatment decision has utilized high-dimensional regression to take into account a large number of patients' covariates and described personalized treatment decision through interactions between treatment and covariates. While a subset of interaction terms can be obtained by existing variable selection methods to indicate relevant covariates for making treatment decision, there often lacks statistical interpretation of the results. This paper proposes an asymptotically unbiased estimator based on Lasso solution for the interaction coefficients. We derive the limiting distribution of the estimator when baseline function of the regression model is unknown and possibly misspecified. Confidence intervals and p-values are derived to infer the effects of the patients' covariates in making treatment decision. We confirm the accuracy of the proposed method and its robustness against misspecified function in simulation and apply the method to STAR*D study for major depression disorder.
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Affiliation(s)
- X Jessie Jeng
- Department of Statistics, North Carolina State University, SAS Hall, 2311 Stinson Dr., Raleigh, NC 27695-8203
| | - Wenbin Lu
- Department of Statistics, North Carolina State University, SAS Hall, 2311 Stinson Dr., Raleigh, NC 27695-8203
| | - Huimin Peng
- Department of Statistics, North Carolina State University, SAS Hall, 2311 Stinson Dr., Raleigh, NC 27695-8203
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18
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Abstract
Finding the optimal treatment regime (or a series of sequential treatment regimes) based on individual characteristics has important applications in areas such as precision medicine, government policies and active labor market interventions. In the current literature, the optimal treatment regime is usually defined as the one that maximizes the average benefit in the potential population. This paper studies a general framework for estimating the quantile-optimal treatment regime, which is of importance in many real-world applications. Given a collection of treatment regimes, we consider robust estimation of the quantile-optimal treatment regime, which does not require the analyst to specify an outcome regression model. We propose an alternative formulation of the estimator as a solution of an optimization problem with an estimated nuisance parameter. This novel representation allows us to investigate the asymptotic theory of the estimated optimal treatment regime using empirical process techniques. We derive theory involving a nonstandard convergence rate and a non-normal limiting distribution. The same nonstandard convergence rate would also occur if the mean optimality criterion is applied, but this has not been studied. Thus, our results fill an important theoretical gap for a general class of policy search methods in the literature. The paper investigates both static and dynamic treatment regimes. In addition, doubly robust estimation and alternative optimality criterion such as that based on Gini's mean difference or weighted quantiles are investigated. Numerical simulations demonstrate the performance of the proposed estimator. A data example from a trial in HIV+ patients is used to illustrate the application.
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Affiliation(s)
- Lan Wang
- School of Statistics, University of Minnesota, Minneapolis, MN 55455
| | - Yu Zhou
- School of Statistics, University of Minnesota, Minneapolis, MN 55455
| | - Rui Song
- Department of Statistics, North Carolina State University, Raleigh, NC 27695
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Moodie EEM, Stephens DA, Alam S, Zhang MJ, Logan B, Arora M, Spellman S, Krakow EF. A cure-rate model for Q-learning: Estimating an adaptive immunosuppressant treatment strategy for allogeneic hematopoietic cell transplant patients. Biom J 2018; 61:442-453. [PMID: 29766558 DOI: 10.1002/bimj.201700181] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Revised: 02/26/2018] [Accepted: 03/23/2018] [Indexed: 11/11/2022]
Abstract
Cancers treated by transplantation are often curative, but immunosuppressive drugs are required to prevent and (if needed) to treat graft-versus-host disease. Estimation of an optimal adaptive treatment strategy when treatment at either one of two stages of treatment may lead to a cure has not yet been considered. Using a sample of 9563 patients treated for blood and bone cancers by allogeneic hematopoietic cell transplantation drawn from the Center for Blood and Marrow Transplant Research database, we provide a case study of a novel approach to Q-learning for survival data in the presence of a potentially curative treatment, and demonstrate the results differ substantially from an implementation of Q-learning that fails to account for the cure-rate.
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Affiliation(s)
- Erica E M Moodie
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, H3A 1A2, Canada
| | - David A Stephens
- Department of Mathematics and Statistics, McGill University, Montreal, QC, H3A 1A2, Canada
| | - Shomoita Alam
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, H3A 1A2, Canada
| | - Mei-Jie Zhang
- Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Brent Logan
- Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Mukta Arora
- Department of Medicine, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Stephen Spellman
- Center for International Blood and Marrow Transplant Research, Minneapolis, MN, 55401, USA
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Liang S, Lu W, Song R, Wang L. Sparse concordance-assisted learning for optimal treatment decision. JOURNAL OF MACHINE LEARNING RESEARCH : JMLR 2018; 18:202. [PMID: 30416396 PMCID: PMC6226264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
To find optimal decision rule, Fan et al. (2016) proposed an innovative concordance-assisted learning algorithm which is based on maximum rank correlation estimator. It makes better use of the available information through pairwise comparison. However the objective function is discontinuous and computationally hard to optimize. In this paper, we consider a convex surrogate loss function to solve this problem. In addition, our algorithm ensures sparsity of decision rule and renders easy interpretation. We derive the L 2 error bound of the estimated coefficients under ultra-high dimension. Simulation results of various settings and application to STAR*D both illustrate that the proposed method can still estimate optimal treatment regime successfully when the number of covariates is large.
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Affiliation(s)
- Shuhan Liang
- Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA
| | - Wenbin Lu
- Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA
| | - Rui Song
- Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA
| | - Lan Wang
- School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA
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21
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Linn KA, Laber EB, Stefanski LA. Interactive Q-learning for Quantiles. J Am Stat Assoc 2017; 112:638-649. [PMID: 28890584 PMCID: PMC5586239 DOI: 10.1080/01621459.2016.1155993] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2014] [Revised: 01/01/2016] [Indexed: 12/18/2022]
Abstract
A dynamic treatment regime is a sequence of decision rules, each of which recommends treatment based on features of patient medical history such as past treatments and outcomes. Existing methods for estimating optimal dynamic treatment regimes from data optimize the mean of a response variable. However, the mean may not always be the most appropriate summary of performance. We derive estimators of decision rules for optimizing probabilities and quantiles computed with respect to the response distribution for two-stage, binary treatment settings. This enables estimation of dynamic treatment regimes that optimize the cumulative distribution function of the response at a prespecified point or a prespecified quantile of the response distribution such as the median. The proposed methods perform favorably in simulation experiments. We illustrate our approach with data from a sequentially randomized trial where the primary outcome is remission of depression symptoms.
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Affiliation(s)
- Kristin A Linn
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104
| | - Eric B Laber
- Department of Statistics, North Carolina State University, Raleigh, NC 27695
| | - Leonard A Stefanski
- Department of Statistics, North Carolina State University, Raleigh, NC 27695
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22
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Ertefaie A, Shortreed S, Chakraborty B. Q-learning residual analysis: application to the effectiveness of sequences of antipsychotic medications for patients with schizophrenia. Stat Med 2016; 35:2221-34. [PMID: 26750518 PMCID: PMC4853263 DOI: 10.1002/sim.6859] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2015] [Accepted: 12/02/2015] [Indexed: 11/12/2022]
Abstract
Q-learning is a regression-based approach that uses longitudinal data to construct dynamic treatment regimes, which are sequences of decision rules that use patient information to inform future treatment decisions. An optimal dynamic treatment regime is composed of a sequence of decision rules that indicate how to optimally individualize treatment using the patients' baseline and time-varying characteristics to optimize the final outcome. Constructing optimal dynamic regimes using Q-learning depends heavily on the assumption that regression models at each decision point are correctly specified; yet model checking in the context of Q-learning has been largely overlooked in the current literature. In this article, we show that residual plots obtained from standard Q-learning models may fail to adequately check the quality of the model fit. We present a modified Q-learning procedure that accommodates residual analyses using standard tools. We present simulation studies showing the advantage of the proposed modification over standard Q-learning. We illustrate this new Q-learning approach using data collected from a sequential multiple assignment randomized trial of patients with schizophrenia. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Ashkan Ertefaie
- Department of Statistics, University of Pennsylvania, Philadelphia, PA, U.S.A
- Center for Pharmacoepidemiology Research and Training, University of Pennsylvania, Philadelphia, PA, U.S.A
| | - Susan Shortreed
- Biostatistics Unit, GroupHealth Research Institute, Seattle, WA, U.S.A
| | - Bibhas Chakraborty
- Centre for Quantitative Medicine, Duke-NUS Graduate Medical School, Singapore
- Department of Biostatistics, Columbia University, New York, NY, U.S.A
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23
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Abstract
Variable selection for optimal treatment regime in a clinical trial or an observational study is getting more attention. Most existing variable selection techniques focused on selecting variables that are important for prediction, therefore some variables that are poor in prediction but are critical for decision-making may be ignored. A qualitative interaction of a variable with treatment arises when treatment effect changes direction as the value of this variable varies. The qualitative interaction indicates the importance of this variable for decision-making. Gunter, Zhu and Murphy (2011) proposed S-score which characterizes the magnitude of qualitative interaction of each variable with treatment individually. In this article, we developed a sequential advantage selection method based on the modified S-score. Our method selects qualitatively interacted variables sequentially, and hence excludes marginally important but jointly unimportant variables or vice versa. The optimal treatment regime based on variables selected via joint model is more comprehensive and reliable. With the proposed stopping criteria, our method can handle a large amount of covariates even if sample size is small. Simulation results show our method performs well in practical settings. We further applied our method to data from a clinical trial for depression.
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Xu Y, Yu M, Zhao YQ, Li Q, Wang S, Shao J. Regularized outcome weighted subgroup identification for differential treatment effects. Biometrics 2015; 71:645-53. [PMID: 25962845 DOI: 10.1111/biom.12322] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2014] [Revised: 12/01/2014] [Accepted: 02/01/2015] [Indexed: 11/27/2022]
Abstract
To facilitate comparative treatment selection when there is substantial heterogeneity of treatment effectiveness, it is important to identify subgroups that exhibit differential treatment effects. Existing approaches model outcomes directly and then define subgroups according to interactions between treatment and covariates. Because outcomes are affected by both the covariate-treatment interactions and covariate main effects, direct modeling outcomes can be hard due to model misspecification, especially in presence of many covariates. Alternatively one can directly work with differential treatment effect estimation. We propose such a method that approximates a target function whose value directly reflects correct treatment assignment for patients. The function uses patient outcomes as weights rather than modeling targets. Consequently, our method can deal with binary, continuous, time-to-event, and possibly contaminated outcomes in the same fashion. We first focus on identifying only directional estimates from linear rules that characterize important subgroups. We further consider estimation of comparative treatment effects for identified subgroups. We demonstrate the advantages of our method in simulation studies and in analyses of two real data sets.
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Affiliation(s)
- Yaoyao Xu
- Department of Statistics, University of Wisconsin, Madison, Wisconsin, U.S.A
| | - Menggang Yu
- Department of Biostatistics & Medical Informatics, University of Wisconsin, Madison, Wisconsin, U.S.A
| | - Ying-Qi Zhao
- Department of Biostatistics & Medical Informatics, University of Wisconsin, Madison, Wisconsin, U.S.A
| | - Quefeng Li
- Department of Operation Research and Financial Engineering, Princeton University, Princeton, New Jersey, U.S.A
| | - Sijian Wang
- Department of Statistics, University of Wisconsin, Madison, Wisconsin, U.S.A.,Department of Biostatistics & Medical Informatics, University of Wisconsin, Madison, Wisconsin, U.S.A
| | - Jun Shao
- Department of Statistics, University of Wisconsin, Madison, Wisconsin, U.S.A
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25
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Song R, Kosorok M, Zeng D, Zhao Y, Laber E, Yuan M. On Sparse representation for Optimal Individualized Treatment Selection with Penalized Outcome Weighted Learning. Stat (Int Stat Inst) 2015; 4:59-68. [PMID: 25883393 DOI: 10.1002/sta4.78] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
As a new strategy for treatment which takes individual heterogeneity into consideration, personalized medicine is of growing interest. Discovering individualized treatment rules (ITRs) for patients who have heterogeneous responses to treatment is one of the important areas in developing personalized medicine. As more and more information per individual is being collected in clinical studies and not all of the information is relevant for treatment discovery, variable selection becomes increasingly important in discovering individualized treatment rules. In this article, we develop a variable selection method based on penalized outcome weighted learning through which an optimal treatment rule is considered as a classification problem where each subject is weighted proportional to his or her clinical outcome. We show that the resulting estimator of the treatment rule is consistent and establish variable selection consistency and the asymptotic distribution of the estimators. The performance of the proposed approach is demonstrated via simulation studies and an analysis of chronic depression data.
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Affiliation(s)
- Rui Song
- Department of Statistics, North Carolina State University, Raleigh, NC 27695
| | - Michael Kosorok
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599
| | - Yingqi Zhao
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, 53792
| | - Eric Laber
- Department of Statistics, North Carolina State University, Raleigh, NC 27695
| | - Ming Yuan
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, 53792
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26
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Abstract
Chronic illness treatment strategies must adapt to the evolving health status of the patient receiving treatment. Data-driven dynamic treatment regimes can offer guidance for clinicians and intervention scientists on how to treat patients over time in order to bring about the most favorable clinical outcome on average. Methods for estimating optimal dynamic treatment regimes, such as Q-learning, typically require modeling nonsmooth, nonmonotone transformations of data. Thus, building well-fitting models can be challenging and in some cases may result in a poor estimate of the optimal treatment regime. Interactive Q-learning (IQ-learning) is an alternative to Q-learning that only requires modeling smooth, monotone transformations of the data. The R package iqLearn provides functions for implementing both the IQ-learning and Q-learning algorithms. We demonstrate how to estimate a two-stage optimal treatment policy with iqLearn using a generated data set bmiData which mimics a two-stage randomized body mass index reduction trial with binary treatments at each stage.
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