1
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Sies A, Doove L, Meers K, Dusseldorp E, Van Mechelen I. Estimating optimal decision trees for treatment assignment: The case of K > 2 treatment alternatives. Behav Res Methods 2024:10.3758/s13428-024-02470-9. [PMID: 39164562 DOI: 10.3758/s13428-024-02470-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/26/2024] [Indexed: 08/22/2024]
Abstract
For many problems in clinical practice, multiple treatment alternatives are available. Given data from a randomized controlled trial or an observational study, an important challenge is to estimate an optimal decision rule that specifies for each client the most effective treatment alternative, given his or her pattern of pretreatment characteristics. In the present paper we will look for such a rule within the insightful family of classification trees. Unfortunately, however, there is dearth of readily accessible software tools for optimal decision tree estimation in the case of more than two treatment alternatives. Moreover, this primary tree estimation problem is also cursed with two secondary problems: a structural missingness in typical studies on treatment evaluation (because every individual is assigned to a single treatment alternative only), and a major issue of replicability. In this paper we propose solutions for both the primary and the secondary problems at stake. We evaluate the proposed solution in a simulation study, and illustrate with an application on the search for an optimal tree-based treatment regime in a randomized controlled trial on K = 3 different types of aftercare for younger women with early-stage breast cancer. We conclude by arguing that the proposed solutions may have relevance for several other classification problems inside and outside the domain of optimal treatment assignment.
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Affiliation(s)
- Aniek Sies
- University of Leuven, Tiensestraat 102 Box 3713, 3000, Leuven, Belgium
| | - Lisa Doove
- University of Leuven, Tiensestraat 102 Box 3713, 3000, Leuven, Belgium
| | - Kristof Meers
- University of Leuven, Tiensestraat 102 Box 3713, 3000, Leuven, Belgium
| | | | - Iven Van Mechelen
- University of Leuven, Tiensestraat 102 Box 3713, 3000, Leuven, Belgium.
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2
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He Q, Zhang S, LeBlanc ML, Zhao YQ. Estimating individualized treatment rules by optimizing the adjusted probability of a longer survival. Stat Methods Med Res 2024:9622802241262525. [PMID: 39053567 DOI: 10.1177/09622802241262525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
Individualized treatment rules inform tailored treatment decisions based on the patient's information, where the goal is to optimize clinical benefit for the population. When the clinical outcome of interest is survival time, most of current approaches typically aim to maximize the expected time of survival. We propose a new criterion for constructing Individualized treatment rules that optimize the clinical benefit with survival outcomes, termed as the adjusted probability of a longer survival. This objective captures the likelihood of living longer with being on treatment, compared to the alternative, which provides an alternative and often straightforward interpretation to communicate with clinicians and patients. We view it as an alternative to the survival analysis standard of the hazard ratio and the increasingly used restricted mean survival time. We develop a new method to construct the optimal Individualized treatment rule by maximizing a nonparametric estimator of the adjusted probability of a longer survival for a decision rule. Simulation studies demonstrate the reliability of the proposed method across a range of different scenarios. We further perform data analysis using data collected from a randomized Phase III clinical trial (SWOG S0819).
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Affiliation(s)
- Qijia He
- Department of Statistics, University of Washington, Seattle, WA, USA
| | | | - Michael L LeBlanc
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Ying-Qi Zhao
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
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3
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Khadem Charvadeh Y, Yi GY. Accommodating misclassification effects on optimizing dynamic treatment regimes with Q-learning. Stat Med 2024; 43:578-605. [PMID: 38213277 DOI: 10.1002/sim.9973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 11/11/2023] [Accepted: 11/17/2023] [Indexed: 01/13/2024]
Abstract
Research on dynamic treatment regimes has enticed extensive interest. Many methods have been proposed in the literature, which, however, are vulnerable to the presence of misclassification in covariates. In particular, although Q-learning has received considerable attention, its applicability to data with misclassified covariates is unclear. In this article, we investigate how ignoring misclassification in binary covariates can impact the determination of optimal decision rules in randomized treatment settings, and demonstrate its deleterious effects on Q-learning through empirical studies. We present two correction methods to address misclassification effects on Q-learning. Numerical studies reveal that misclassification in covariates induces non-negligible estimation bias and that the correction methods successfully ameliorate bias in parameter estimation.
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Affiliation(s)
- Yasin Khadem Charvadeh
- Department of Statistical and Actuarial Sciences, University of Western Ontario, London, Ontario, Canada
| | - Grace Y Yi
- Department of Statistical and Actuarial Sciences, University of Western Ontario, London, Ontario, Canada
- Department of Computer Science, University of Western Ontario, London, Ontario, Canada
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4
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Liang M, Yu M. Relative contrast estimation and inference for treatment recommendation. Biometrics 2023; 79:2920-2932. [PMID: 36645310 DOI: 10.1111/biom.13826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 12/29/2022] [Indexed: 01/17/2023]
Abstract
When there are resource constraints, it may be necessary to rank individualized treatment benefits to facilitate the prioritization of assigning different treatments. Most existing literature on individualized treatment rules targets absolute conditional treatment effect differences as a metric for the benefit. However, there can be settings where relative differences may better represent such benefit. In this paper, we consider modeling such relative differences formed as scale-invariant contrasts between the conditional treatment effects. By showing that all scale-invariant contrasts are monotonic transformations of each other, we posit a single index model for a particular relative contrast. We then characterize semiparametric estimating equations, including the efficient score, to estimate index parameters. To achieve semiparametric efficiency, we propose a two-step approach that minimizes a doubly robust loss function for initial estimation and then performs a one-step efficiency augmentation procedure. Careful theoretical and numerical studies are provided to show the superiority of our proposed approach.
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Affiliation(s)
- Muxuan Liang
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Menggang Yu
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin, USA
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5
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Zhang Y, Vock DM, Patrick ME, Murray TA. Modified interactive Q-learning for attenuating the impact of model misspecification with treatment effect heterogeneity. Stat Methods Med Res 2023; 32:2240-2253. [PMID: 37859598 PMCID: PMC10683339 DOI: 10.1177/09622802231206471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
A sequential multiple assignment randomized trial, which incorporates multiple stages of randomization, is a popular approach for collecting data to inform personalized and adaptive treatments. There is an extensive literature on statistical methods to analyze data collected in sequential multiple assignment randomized trials and estimate the optimal dynamic treatment regime. Q-learning with linear regression is widely used for this purpose due to its ease of implementation. However, model misspecification is a common problem with this approach, and little attention has been given to the impact of model misspecification when treatment effects are heterogeneous across subjects. This article describes the integrative impact of two possible types of model misspecification related to treatment effect heterogeneity: omitted early-stage treatment effects in late-stage main effect model, and violated linearity assumption between pseudo-outcomes and predictors despite non-linearity arising from the optimization operation. The proposed method, aiming to deal with both types of misspecification concomitantly, builds interactive models into modified parametric Q-learning with Murphy's regret function. Simulations show that the proposed method is robust to both sources of model misspecification. The proposed method is applied to a two-stage sequential multiple assignment randomized trial with embedded tailoring aimed at reducing binge drinking in first-year college students.
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Affiliation(s)
- Yuan Zhang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - David M Vock
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Megan E Patrick
- Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Thomas A Murray
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
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6
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Rose EJ, Moodie EEM, Shortreed SM. Monte Carlo sensitivity analysis for unmeasured confounding in dynamic treatment regimes. Biom J 2023; 65:e2100359. [PMID: 37017498 PMCID: PMC11426919 DOI: 10.1002/bimj.202100359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 09/16/2022] [Accepted: 09/20/2022] [Indexed: 04/06/2023]
Abstract
Data-driven methods for personalizing treatment assignment have garnered much attention from clinicians and researchers. Dynamic treatment regimes formalize this through a sequence of decision rules that map individual patient characteristics to a recommended treatment. Observational studies are commonly used for estimating dynamic treatment regimes due to the potentially prohibitive costs of conducting sequential multiple assignment randomized trials. However, estimating a dynamic treatment regime from observational data can lead to bias in the estimated regime due to unmeasured confounding. Sensitivity analyses are useful for assessing how robust the conclusions of the study are to a potential unmeasured confounder. A Monte Carlo sensitivity analysis is a probabilistic approach that involves positing and sampling from distributions for the parameters governing the bias. We propose a method for performing a Monte Carlo sensitivity analysis of the bias due to unmeasured confounding in the estimation of dynamic treatment regimes. We demonstrate the performance of the proposed procedure with a simulation study and apply it to an observational study examining tailoring the use of antidepressant medication for reducing symptoms of depression using data from Kaiser Permanente Washington.
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Affiliation(s)
- Eric J Rose
- Department of Epidemiology and Biostatistics, McGill University, Montreal, QC, Canada
- Department of Epidemiology and Biostatistics, University at Albany, Rensselaer, New York, USA
| | - Erica E M Moodie
- Department of Epidemiology and Biostatistics, McGill University, Montreal, QC, Canada
| | - Susan M Shortreed
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
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7
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Rose EJ, Moodie EEM, Shortreed S. Using Pilot Data for Power Analysis of Observational Studies for the Estimation of Dynamic Treatment Regimes. OBSERVATIONAL STUDIES 2023; 9:25-48. [PMID: 39005256 PMCID: PMC11245299 DOI: 10.1353/obs.2023.a906627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Significant attention has been given to developing data-driven methods for tailoring patient care based on individual patient characteristics. Dynamic treatment regimes formalize this approach through a sequence of decision rules that map patient information to a suggested treatment. The data for estimating and evaluating treatment regimes are ideally gathered through the use of Sequential Multiple Assignment Randomized Trials (SMARTs), though longitudinal observational studies are commonly used due to the potentially prohibitive costs of conducting a SMART. Observational studies are typically powered for simple comparisons of fixed treatment sequences; a priori power or sample size calculations for tailored strategies are rarely if ever undertaken. This has lead to many studies that fail to find a statistically significant benefit to tailoring treatment. We develop power analyses for the estimation of dynamic treatment regimes from observational studies. Our approach uses pilot data to estimate the power for comparing the value of the optimal regime, i.e., the expected outcome if all patients in the population were treated by following the optimal regime, with a known comparison mean. This allows for calculations that ensure a study has sufficient power to detect the need for tailoring, should it be present. Our approach also ensures the value of the estimated optimal treatment regime has a high probability of being within a range of the value of the true optimal regime, set a priori. We examine the performance of the proposed procedure with a simulation study and use it to size a study for reducing depressive symptoms using data from electronic health records.
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Affiliation(s)
- Eric J Rose
- Department of Epidemiology and Biostatistics, University at Albany, Rensselaer, NY, 12144, USA
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, H3A 1G1, Canada
| | - Erica E M Moodie
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, H3A 1G1, Canada
| | - Susan Shortreed
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, 98101, USA
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
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8
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Liao P, Qi Z, Wan R, Klasnja P, Murphy SA. Batch policy learning in average reward Markov decision processes. Ann Stat 2022; 50:3364-3387. [PMID: 37022318 PMCID: PMC10072865 DOI: 10.1214/22-aos2231] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
We consider the batch (off-line) policy learning problem in the infinite horizon Markov Decision Process. Motivated by mobile health applications, we focus on learning a policy that maximizes the long-term average reward. We propose a doubly robust estimator for the average reward and show that it achieves semiparametric efficiency. Further we develop an optimization algorithm to compute the optimal policy in a parameterized stochastic policy class. The performance of the estimated policy is measured by the difference between the optimal average reward in the policy class and the average reward of the estimated policy and we establish a finite-sample regret guarantee. The performance of the method is illustrated by simulation studies and an analysis of a mobile health study promoting physical activity.
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Affiliation(s)
- Peng Liao
- Department of Statistics, Harvard University
| | - Zhengling Qi
- Department of Decision Sciences, George Washington University
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9
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Qian W, Ing CK, Liu J. Adaptive Algorithm for Multi-armed Bandit Problem with High-dimensional Covariates. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2152343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Affiliation(s)
- Wei Qian
- Department of Applied Economics and Statistics, University of Delaware, Newark, DE
| | - Ching-Kang Ing
- Institute of Statistics, National Tsing Hua University, Hsinchu, Taiwan
| | - Ji Liu
- Meta Platforms, Inc., Seattle, WA
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10
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Oh EJ, Qian M, Cheung YK. Generalization error bounds of dynamic treatment regimes in penalized regression-based learning. Ann Stat 2022. [DOI: 10.1214/22-aos2171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Eun Jeong Oh
- Department of Biostatistics, Columbia University
| | - Min Qian
- Department of Biostatistics, Columbia University
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11
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Effects of a continuous nursing care model on elderly patients with total hip arthroplasty: a randomized controlled trial. Aging Clin Exp Res 2022; 34:1603-1611. [PMID: 34476774 DOI: 10.1007/s40520-021-01965-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 08/14/2021] [Indexed: 01/05/2023]
Abstract
BACKGROUND Continuous nursing care (CNC) is an extended service based on meeting the needs of discharged patients for post-discharge treatment and rehabilitation. This research aimed to investigate the effects of CNC on older patients with total hip arthroplasty and to offer a scientific basis for improving the prognosis. METHODS A total of 134 patients with total hip arthroplasty were randomly divided into the control group (n = 67) and the intervention group (n = 67). The control group was treated by conventional nursing care and the intervention group was treated by CNC. Harris hip score, Barthel index, the activities of daily living (ADL) scale, self-rating depression scale (SDS) and self-rating anxiety scale (SAS) in these two groups were evaluated. Demographic characteristics between groups were analyzed by unpaired t test. The observation indexes between groups were assessed by two-way ANOVA test followed by Tukey's multiple comparisons test. RESULTS The scores of Harris hip score, Barthel index, ADL, SDS and SAS in the intervention group after intervention and after follow-up were better than the intervention group before intervention (all p < 0.01). Meanwhile, the scores of Harris hip score, Barthel index, ADL, SDS and SAS in the intervention group were better than the control group both after intervention and after follow-up (all p < 0.01). CONCLUSION In conclusion, CNC showed better efficacy than conventional nursing care in promoting hip joint function recovery, improving quality of life and alleviating anxiety and depression for older patients with total hip arthroplasty.
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12
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Liang M, Choi YG, Ning Y, Smith MA, Zhao YQ. Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score. JOURNAL OF MACHINE LEARNING RESEARCH : JMLR 2022; 23:262. [PMID: 38098839 PMCID: PMC10720606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
With the increasing adoption of electronic health records, there is an increasing interest in developing individualized treatment rules, which recommend treatments according to patients' characteristics, from large observational data. However, there is a lack of valid inference procedures for such rules developed from this type of data in the presence of high-dimensional covariates. In this work, we develop a penalized doubly robust method to estimate the optimal individualized treatment rule from high-dimensional data. We propose a split-and-pooled de-correlated score to construct hypothesis tests and confidence intervals. Our proposal adopts the data splitting to conquer the slow convergence rate of nuisance parameter estimations, such as non-parametric methods for outcome regression or propensity models. We establish the limiting distributions of the split-and-pooled de-correlated score test and the corresponding one-step estimator in high-dimensional setting. Simulation and real data analysis are conducted to demonstrate the superiority of the proposed method.
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Affiliation(s)
- Muxuan Liang
- Department of Biostatistics, University of Florida, Gainesville, Florida 32611, USA
| | - Young-Geun Choi
- Department of Statistics, Sookmyung Women's University, Seoul 04310, Korea
| | - Yang Ning
- Department of Statistics and Data Science, Cornell University, Ithaca, Newyork 14853, USA
| | - Maureen A Smith
- Departments of Population Health and Family Medicine, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Ying-Qi Zhao
- Public Health Sciences Divisions, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
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13
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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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14
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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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15
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Dong L, Laber E, Goldberg Y, Song R, Yang S. Ascertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness. Stat Med 2020; 39:3503-3520. [PMID: 32729973 DOI: 10.1002/sim.8678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 04/28/2020] [Accepted: 04/30/2020] [Indexed: 11/10/2022]
Abstract
Dynamic treatment regimes operationalize precision medicine as a sequence of decision rules, one per stage of clinical intervention, that map up-to-date patient information to a recommended intervention. An optimal treatment regime maximizes the mean utility when applied to the population of interest. Methods for estimating an optimal treatment regime assume the data to be fully observed, which rarely occurs in practice. A common approach is to first use multiple imputation and then pool the estimators across imputed datasets. However, this approach requires estimating the joint distribution of patient trajectories, which can be high-dimensional, especially when there are multiple stages of intervention. We examine the application of inverse probability weighted estimating equations as an alternative to multiple imputation in the context of monotonic missingness. This approach applies to a broad class of estimators of an optimal treatment regime including both Q-learning and a generalization of outcome weighted learning. We establish consistency under mild regularity conditions and demonstrate its advantages in finite samples using a series of simulation experiments and an application to a schizophrenia study.
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Affiliation(s)
- Lin Dong
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| | - Eric Laber
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| | - Yair Goldberg
- Department of Statistics, Technion Israel Institute of Technology, Haifa, Israel
| | - Rui Song
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| | - Shu Yang
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
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16
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Wu Y, Wang L. Resampling-based confidence intervals for model-free robust inference on optimal treatment regimes. Biometrics 2020; 77:465-476. [PMID: 32687215 DOI: 10.1111/biom.13337] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 06/24/2020] [Indexed: 12/01/2022]
Abstract
We propose a new procedure for inference on optimal treatment regimes in the model-free setting, which does not require to specify an outcome regression model. Existing model-free estimators for optimal treatment regimes are usually not suitable for the purpose of inference, because they either have nonstandard asymptotic distributions or do not necessarily guarantee consistent estimation of the parameter indexing the Bayes rule due to the use of surrogate loss. We first study a smoothed robust estimator that directly targets the parameter corresponding to the Bayes decision rule for optimal treatment regimes estimation. This estimator is shown to have an asymptotic normal distribution. Furthermore, we verify that a resampling procedure provides asymptotically accurate inference for both the parameter indexing the optimal treatment regime and the optimal value function. A new algorithm is developed to calculate the proposed estimator with substantially improved speed and stability. Numerical results demonstrate the satisfactory performance of the new methods.
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Affiliation(s)
- Yunan Wu
- School of Statistics, University of Minnesota, Minneapolis, Minnesota
| | - Lan Wang
- Department of Management Science, University of Miami, Coral Gables, Florida
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17
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Cui Y, Tchetgen ET. A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity. J Am Stat Assoc 2020; 116:162-173. [PMID: 33994604 PMCID: PMC8118566 DOI: 10.1080/01621459.2020.1783272] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2019] [Revised: 02/05/2020] [Accepted: 06/09/2020] [Indexed: 01/23/2023]
Abstract
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 approach to learning optimal treatment regimes under endogeneity. Specifically, we establish identification of both value function E [ Y D ( L ) ] for a given regime D and optimal regimes arg max D E [ Y D ( L ) ] with the aid of a binary instrumental variable, when no unmeasured confounding fails to hold. We also construct novel multiply robust classification-based estimators. Furthermore, we propose to identify and estimate optimal treatment regimes among those who would comply to the assigned treatment under a monotonicity assumption. In this latter case, we establish the somewhat surprising result that complier optimal regimes can be consistently estimated without directly collecting compliance information and therefore without the complier average treatment effect itself being identified. Our approach is illustrated via extensive simulation studies and a data application on the effect of child rearing on labor participation.
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Affiliation(s)
- Yifan Cui
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104
| | - Eric Tchetgen Tchetgen
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104
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18
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Affiliation(s)
- Nathan Kallus
- School of Operations Research and Information Engineering and Cornell Tech, Cornell University, New York, NY
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19
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Sherman ES, Arbour D, Shpitser I. General Identification of Dynamic Treatment Regimes Under Interference. PROCEEDINGS OF MACHINE LEARNING RESEARCH 2020; 108:3917-3927. [PMID: 33313513 PMCID: PMC7730527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In many applied fields, researchers are often interested in tailoring treatments to unit-level characteristics in order to optimize an outcome of interest. Methods for identifying and estimating treatment policies are the subject of the dynamic treatment regime literature. Separately, in many settings the assumption that data are independent and identically distributed does not hold due to inter-subject dependence. The phenomenon where a subject's outcome is dependent on his neighbor's exposure is known as interference. These areas intersect in myriad real-world settings. In this paper we consider the problem of identifying optimal treatment policies in the presence of interference. Using a general representation of interference, via Lauritzen-Wermuth-Freydenburg chain graphs (Lauritzen and Richardson, 2002), we formalize a variety of policy interventions under interference and extend existing identification theory (Tian, 2008; Sherman and Shpitser, 2018). Finally, we illustrate the efficacy of policy maximization under interference in a simulation study.
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20
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Artman WJ, Nahum-Shani I, Wu T, Mckay JR, Ertefaie A. Power analysis in a SMART design: sample size estimation for determining the best embedded dynamic treatment regime. Biostatistics 2020; 21:432-448. [PMID: 30380020 PMCID: PMC7307973 DOI: 10.1093/biostatistics/kxy064] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 09/21/2018] [Accepted: 10/07/2018] [Indexed: 01/15/2023] Open
Abstract
Sequential, multiple assignment, randomized trial (SMART) designs have become increasingly popular in the field of precision medicine by providing a means for comparing more than two sequences of treatments tailored to the individual patient, i.e., dynamic treatment regime (DTR). The construction of evidence-based DTRs promises a replacement to ad hoc one-size-fits-all decisions pervasive in patient care. However, there are substantial statistical challenges in sizing SMART designs due to the correlation structure between the DTRs embedded in the design (EDTR). Since a primary goal of SMARTs is the construction of an optimal EDTR, investigators are interested in sizing SMARTs based on the ability to screen out EDTRs inferior to the optimal EDTR by a given amount which cannot be done using existing methods. In this article, we fill this gap by developing a rigorous power analysis framework that leverages the multiple comparisons with the best methodology. Our method employs Monte Carlo simulation to compute the number of individuals to enroll in an arbitrary SMART. We evaluate our method through extensive simulation studies. We illustrate our method by retrospectively computing the power in the Extending Treatment Effectiveness of Naltrexone (EXTEND) trial. An R package implementing our methodology is available to download from the Comprehensive R Archive Network.
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Affiliation(s)
- William J Artman
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, Saunders Research Building, 265 Crittenden Blvd., NY, USA
| | - Inbal Nahum-Shani
- Institute for Social Research, University of Michigan, 426 Thompson St, Ann Arbor, MI, USA
| | - Tianshuang Wu
- AbbVie Inc., 1 North Waukegan Road, North Chicago, IL, USA
| | - James R Mckay
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3535 Market St., Suite 500, Philadelphia, PA, USA
| | - Ashkan Ertefaie
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Saunders Research Building, 265 Crittenden Blvd., Rochester, NY, USA
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21
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Ertefaie A, McKay JR, Oslin D, Strawderman RL. Robust Q-learning. J Am Stat Assoc 2020; 116:368-381. [PMID: 34121784 PMCID: PMC8190585 DOI: 10.1080/01621459.2020.1753522] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Revised: 03/12/2020] [Accepted: 04/04/2020] [Indexed: 10/24/2022]
Abstract
Q-learning is a regression-based approach that is widely used to formalize the development of an optimal dynamic treatment strategy. Finite dimensional working models are typically used to estimate certain nuisance parameters, and misspecification of these working models can result in residual confounding and/or efficiency loss. We propose a robust Q-learning approach which allows estimating such nuisance parameters using data-adaptive techniques. We study the asymptotic behavior of our estimators and provide simulation studies that highlight the need for and usefulness of the proposed method in practice. We use the data from the "Extending Treatment Effectiveness of Naltrexone" multi-stage randomized trial to illustrate our proposed methods.
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Affiliation(s)
- Ashkan Ertefaie
- Department of Biostatistics and Computational Biology, University of Rochester
| | - James R McKay
- Center on the Continuum of Care in the Addictions, Department of Psychiatry, University of Pennsylvania
| | - David Oslin
- Philadelphia Veterans Administration Medical Center, and Treatment Research Center and Center for Studies of Addictions, Department of Psychiatry, University of Pennsylvania
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22
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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 2020; 25:182-205. [PMID: 31497981 PMCID: PMC7061067 DOI: 10.1037/met0000232] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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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Affiliation(s)
- Palash Ghosh
- Centre for Quantitative Medicine, Duke-NUS Medical School,
National University of Singapore, Singapore
| | | | - Bonnie Spring
- Center for Behavior and Health, Northwestern University
Feinberg School of Medicine
| | - Bibhas Chakraborty
- Centre for Quantitative Medicine, Duke-NUS Medical School,
National University of Singapore, Singapore
- Department of Statistics and Applied Probability, National
University of Singapore
- Department of Biostatistics and Bioinformatics, Duke
University
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23
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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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24
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Sies A, Van Mechelen I. Estimating the quality of optimal treatment regimes. Stat Med 2019; 38:4925-4938. [PMID: 31424128 DOI: 10.1002/sim.8342] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Revised: 07/13/2019] [Accepted: 07/18/2019] [Indexed: 11/08/2022]
Abstract
When multiple treatment alternatives are available for a disease, an obvious question is which alternative is most effective for which patient. One may address this question by searching for optimal treatment regimes that specify for each individual the preferable treatment alternative based on that individual's baseline characteristics. When such a regime has been estimated, its quality (in terms of the expected outcome if it was used for treatment assignment of all patients in the population under study) is of obvious interest. Obtaining a good and reliable estimate of this quantity is a key challenge for which so far no satisfactory solution is available. In this paper, we consider for this purpose several estimators of the expected outcome in conjunction with several resampling methods. The latter have been evaluated before within the context of statistical learning to estimate the prediction error of estimated prediction rules. Yet, the results of these evaluations were equivocal, with different best performing methods in different studies, and with near-zero and even negative correlations between true and estimated prediction errors. Moreover, for different reasons, it is not straightforward to extrapolate the findings of these studies to the context of optimal treatment regimes. To address these issues, we set up a new and comprehensive simulation study. In this study, combinations of different estimators with .632+ and out-of-bag bootstrap resampling methods performed best. In addition, the study shed a surprising new light on the previously reported problematic correlations between true and estimated prediction errors in the area of statistical learning.
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Affiliation(s)
- Aniek Sies
- Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
| | - Iven Van Mechelen
- Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
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25
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Guan Q, Reich BJ, Laber EB, Bandyopadhyay D. Bayesian Nonparametric Policy Search with Application to Periodontal Recall Intervals. J Am Stat Assoc 2019; 115:1066-1078. [PMID: 33012901 PMCID: PMC7531024 DOI: 10.1080/01621459.2019.1660169] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Revised: 07/18/2019] [Accepted: 08/05/2019] [Indexed: 10/26/2022]
Abstract
Tooth loss from periodontal disease is a major public health burden in the United States. Standard clinical practice is to recommend a dental visit every six months; however, this practice is not evidence-based, and poor dental outcomes and increasing dental insurance premiums indicate room for improvement. We consider a tailored approach that recommends recall time based on patient characteristics and medical history to minimize disease progression without increasing resource expenditures. We formalize this method as a dynamic treatment regime which comprises a sequence of decisions, one per stage of intervention, that follow a decision rule which maps current patient information to a recommendation for their next visit time. The dynamics of periodontal health, visit frequency, and patient compliance are complex, yet the estimated optimal regime must be interpretable to domain experts if it is to be integrated into clinical practice. We combine non-parametric Bayesian dynamics modeling with policy-search algorithms to estimate the optimal dynamic treatment regime within an interpretable class of regimes. Both simulation experiments and application to a rich database of electronic dental records from the HealthPartners HMO shows that our proposed method leads to better dental health without increasing the average recommended recall time relative to competing methods.
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Affiliation(s)
- Qian Guan
- Department of Statistics, North Carolina State University, Raleigh, North Carolina
| | - Brian J. Reich
- Department of Statistics, North Carolina State University, Raleigh, North Carolina
| | - Eric B. Laber
- Department of Statistics, North Carolina State University, Raleigh, North Carolina
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26
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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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27
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Sies A, Demyttenaere K, Van Mechelen I. Studying treatment-effect heterogeneity in precision medicine through induced subgroups. J Biopharm Stat 2019; 29:491-507. [PMID: 30794033 DOI: 10.1080/10543406.2019.1579220] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Precision medicine, in the sense of tailoring the choice of medical treatment to patients' pretreatment characteristics, is nowadays gaining a lot of attention. Preferably, this tailoring should be realized in an evidence-based way, with key evidence in this regard pertaining to subgroups of patients that respond differentially to treatment (i.e., to subgroups involved in treatment-subgroup interactions). Often a-priori hypotheses on subgroups involved in treatment-subgroup interactions are lacking or are incomplete at best. Therefore, methods are needed that can induce such subgroups from empirical data on treatment effectiveness in a post hoc manner. Recently, quite a few such methods have been developed. So far, however, there is little empirical experience in their usage. This may be problematic for medical statisticians and statistically minded medical researchers, as many (nontrivial) choices have to be made during the data-analytic process. The main purpose of this paper is to discuss the major concepts and considerations when using these methods. This discussion will be based on a systematic, conceptual, and technical analysis of the type of research questions at play, and of the type of data that the methods can handle along with the available software, and a review of available empirical evidence. We will illustrate all this with the analysis of a dataset comparing several anti-depressant treatments.
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Affiliation(s)
- Aniek Sies
- a Faculty of Psychology and Educational Sciences , KU Leuven , Leuven , Belgium
| | | | - Iven Van Mechelen
- a Faculty of Psychology and Educational Sciences , KU Leuven , Leuven , Belgium
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28
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Goetz LH, Schork NJ. Personalized medicine: motivation, challenges, and progress. Fertil Steril 2019; 109:952-963. [PMID: 29935653 DOI: 10.1016/j.fertnstert.2018.05.006] [Citation(s) in RCA: 263] [Impact Index Per Article: 52.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 05/04/2018] [Accepted: 05/04/2018] [Indexed: 01/07/2023]
Abstract
There is a great deal of hype surrounding the concept of personalized medicine. Personalized medicine is rooted in the belief that since individuals possess nuanced and unique characteristics at the molecular, physiological, environmental exposure, and behavioral levels, they may need to have interventions provided to them for diseases they possess that are tailored to these nuanced and unique characteristics. This belief has been verified to some degree through the application of emerging technologies such as DNA sequencing, proteomics, imaging protocols, and wireless health monitoring devices, which have revealed great inter-individual variation in disease processes. In this review, we consider the motivation for personalized medicine, its historical precedents, the emerging technologies that are enabling it, some recent experiences including successes and setbacks, ways of vetting and deploying personalized medicines, and future directions, including potential ways of treating individuals with fertility and sterility issues. We also consider current limitations of personalized medicine. We ultimately argue that since aspects of personalized medicine are rooted in biological realities, personalized medicine practices in certain contexts are likely to be inevitable, especially as relevant assays and deployment strategies become more efficient and cost-effective.
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Affiliation(s)
| | - Nicholas J Schork
- The Translational Genomics Research Institute, Phoenix, Arizona; The City of Hope/TGen IMPACT Center, Duarte, California; J. Craig Venter Institute, La Jolla, California; The University of California, San Diego, La Jolla, California.
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29
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Zhao YQ, Laber EB, Ning Y, Saha S, Sands BE. Efficient augmentation and relaxation learning for individualized treatment rules using observational data. JOURNAL OF MACHINE LEARNING RESEARCH : JMLR 2019; 20:48. [PMID: 31440118 PMCID: PMC6705615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Individualized treatment rules aim to identify if, when, which, and to whom treatment should be applied. A globally aging population, rising healthcare costs, and increased access to patient-level data have created an urgent need for high-quality estimators of individualized treatment rules that can be applied to observational data. A recent and promising line of research for estimating individualized treatment rules recasts the problem of estimating an optimal treatment rule as a weighted classification problem. We consider a class of estimators for optimal treatment rules that are analogous to convex large-margin classifiers. The proposed class applies to observational data and is doubly-robust in the sense that correct specification of either a propensity or outcome model leads to consistent estimation of the optimal individualized treatment rule. Using techniques from semiparametric efficiency theory, we derive rates of convergence for the proposed estimators and use these rates to characterize the bias-variance trade-off for estimating individualized treatment rules with classification-based methods. Simulation experiments informed by these results demonstrate that it is possible to construct new estimators within the proposed framework that significantly outperform existing ones. We illustrate the proposed methods using data from a labor training program and a study of inflammatory bowel syndrome.
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Affiliation(s)
- Ying-Qi Zhao
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Eric B Laber
- Department of Statistics, North Carolina State University, Raleigh, NC, 27695, USA
| | - Yang Ning
- Department of Statistical Science, Cornell University, Ithaca, NY, 14853, USA
| | - Sumona Saha
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, 53705, USA
| | - Bruce E Sands
- Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
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30
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Qian M, Cheng B. Discussion of Entropy Learning for Dynamic Treatment Regimes. Stat Sin 2019; 29:1662-1665. [PMID: 31680758 PMCID: PMC6824196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Affiliation(s)
- Min Qian
- Department of Biostatistics, Columbia University, 722 West 168th Street, New York City, NY 10032, USA
| | - Bin Cheng
- Department of Biostatistics, Columbia University, 722 West 168th Street, New York City, NY 10032, USA
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31
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Fan Y, He M, Su L, Zhou X. A smoothed
Q
‐learning algorithm for estimating optimal dynamic treatment regimes. Scand Stat Theory Appl 2018. [DOI: 10.1111/sjos.12359] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yanqin Fan
- Department of EconomicsUniversity of Washington Seattle Washington
| | - Ming He
- Economics Discipline GroupUniversity of Technology Sydney Ultimo Australia
| | - Liangjun Su
- School of EconomicsSingapore Management University Singapore
| | - Xiao‐Hua Zhou
- Beijing International Center for Mathematical ResearchPeking University Beijing China
- School of Public HealthPeking University Beijing China
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32
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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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33
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Murray TA, Yuan Y, Thall PF. A Bayesian Machine Learning Approach for Optimizing Dynamic Treatment Regimes. J Am Stat Assoc 2018; 113:1255-1267. [PMID: 30739965 DOI: 10.1080/01621459.2017.1340887] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Medical therapy often consists of multiple stages, with a treatment chosen by the physician at each stage based on the patient's history of treatments and clinical outcomes. These decisions can be formalized as a dynamic treatment regime. This paper describes a new approach for optimizing dynamic treatment regimes that bridges the gap between Bayesian inference and existing approaches, like Q-learning. The proposed approach fits a series of Bayesian regression models, one for each stage, in reverse sequential order. Each model uses as a response variable the remaining payoff assuming optimal actions are taken at subsequent stages, and as covariates the current history and relevant actions at that stage. The key difficulty is that the optimal decision rules at subsequent stages are unknown, and even if these decision rules were known the relevant response variables may be counterfactual. However, posterior distributions can be derived from the previously fitted regression models for the optimal decision rules and the counterfactual response variables under a particular set of rules. The proposed approach averages over these posterior distributions when fitting each regression model. An efficient sampling algorithm for estimation is presented, along with simulation studies that compare the proposed approach with Q-learning.
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Affiliation(s)
| | - Ying Yuan
- Department of Biostatistics, MD Anderson Cancer Center
| | - Peter F Thall
- Department of Biostatistics, MD Anderson Cancer Center
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34
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Ertefaie A, Strawderman RL. Constructing dynamic treatment regimes over indefinite time horizons. Biometrika 2018. [DOI: 10.1093/biomet/asy043] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Ashkan Ertefaie
- Department of Biostatistics and Computational Biology, University of Rochester, 265 Crittenden Boulevard, Rochester, New York, U.S.A
| | - Robert L Strawderman
- Department of Biostatistics and Computational Biology, University of Rochester, 265 Crittenden Boulevard, Rochester, New York, U.S.A
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35
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Pollack AH, Oron AP, Flynn JT, Munshi R. Using dynamic treatment regimes to understand erythropoietin-stimulating agent hyporesponsiveness. Pediatr Nephrol 2018; 33:1411-1417. [PMID: 29619552 PMCID: PMC6827568 DOI: 10.1007/s00467-018-3948-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Revised: 03/19/2018] [Accepted: 03/20/2018] [Indexed: 11/27/2022]
Abstract
BACKGROUND Erythropoietin-stimulating agent hyporesponsiveness (ESAH) is associated with increased cardiovascular mortality in patients with end-stage renal disease (ESRD) on hemodialysis. Dynamic treatment regimes (DTR), a clinical decision support (CDS) tool that guides the prescription of specific therapies in response to variations in patient states, have been used to guide treatment for chronic illnesses that require frequent monitoring and therapy changes. Our objective is to explore the role of utilizing a DTR to reduce ESAH in pediatric hemodialysis patients. METHODS Retrospective analysis of ESRD patients on hemodialysis who received ESAs. Dosing was adjusted using a locally developed protocol designed to target a hemoglobin between 10 and 12 g/dl. Analyzing this protocol as a DTR, we assessed adherence to the protocol over time measuring how the hyporesponse index (ESA dose/hemoglobin value) changed due to varying levels of adherence. RESULTS Eighteen patients met study criteria. Median hemoglobin was 11.4 g/dl (range 6.1-15.4), and median weekly ESA dose (darbepoetin-equivalent) was 0.4 mcg/kg/dose (range 0-2.1). Full adherence to the DTR was identified in 266 (71%) of the 4-week periods, with a median average adherence score of 0.80 (range 0.63-0.91). As adherence to the DTR improved, ESAH decreased. During the last 12 weeks, 13 out of 18 patients had lower average ESA/hemoglobin ratio than the first 12 weeks. CONCLUSIONS A DTR appears to be well-suited to the treatment of anemia in ESRD and reduces ESAH. Our work shows the potential of DTRs to drive the development and evaluation of clinical practice guidelines.
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Affiliation(s)
- Ari H Pollack
- Division of Nephrology, Seattle Children's Hospital, Seattle, WA, USA.
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA.
| | - Assaf P Oron
- Section of Epidemiology, Institute for Disease Modeling, Bellevue, WA, USA
| | - Joseph T Flynn
- Division of Nephrology, Seattle Children's Hospital, Seattle, WA, USA
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
| | - Raj Munshi
- Division of Nephrology, Seattle Children's Hospital, Seattle, WA, USA
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
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36
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Newsome SJ, Keogh RH, Daniel RM. Estimating long-term treatment effects in observational data: A comparison of the performance of different methods under real-world uncertainty. Stat Med 2018; 37:2367-2390. [PMID: 29671915 PMCID: PMC6001810 DOI: 10.1002/sim.7664] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Revised: 01/19/2018] [Accepted: 02/26/2018] [Indexed: 11/29/2022]
Abstract
In the presence of time-dependent confounding, there are several methods available to estimate treatment effects. With correctly specified models and appropriate structural assumptions, any of these methods could provide consistent effect estimates, but with real-world data, all models will be misspecified and it is difficult to know if assumptions are violated. In this paper, we investigate five methods: inverse probability weighting of marginal structural models, history-adjusted marginal structural models, sequential conditional mean models, g-computation formula, and g-estimation of structural nested models. This work is motivated by an investigation of the effects of treatments in cystic fibrosis using the UK Cystic Fibrosis Registry data focussing on two outcomes: lung function (continuous outcome) and annual number of days receiving intravenous antibiotics (count outcome). We identified five features of this data that may affect the performance of the methods: misspecification of the causal null, long-term treatment effects, effect modification by time-varying covariates, misspecification of the direction of causal pathways, and censoring. In simulation studies, under ideal settings, all five methods provide consistent estimates of the treatment effect with little difference between methods. However, all methods performed poorly under some settings, highlighting the importance of using appropriate methods based on the data available. Furthermore, with the count outcome, the issue of non-collapsibility makes comparison between methods delivering marginal and conditional effects difficult. In many situations, we would recommend using more than one of the available methods for analysis, as if the effect estimates are very different, this would indicate potential issues with the analyses.
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Affiliation(s)
- Simon J. Newsome
- Department of Medical StatisticsLondon School of Hygiene and Tropical MedicineLondonUK
| | - Ruth H. Keogh
- Department of Medical StatisticsLondon School of Hygiene and Tropical MedicineLondonUK
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37
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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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38
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Laber EB, Wu F, Munera C, Lipkovich I, Colucci S, Ripa S. Identifying optimal dosage regimes under safety constraints: An application to long term opioid treatment of chronic pain. Stat Med 2018; 37:1407-1418. [PMID: 29468702 PMCID: PMC6293986 DOI: 10.1002/sim.7566] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Revised: 08/26/2017] [Accepted: 10/30/2017] [Indexed: 11/08/2022]
Abstract
There is growing interest and investment in precision medicine as a means to provide the best possible health care. A treatment regime formalizes precision medicine as a sequence of decision rules, one per clinical intervention period, that specify if, when and how current treatment should be adjusted in response to a patient's evolving health status. It is standard to define a regime as optimal if, when applied to a population of interest, it maximizes the mean of some desirable clinical outcome, such as efficacy. However, in many clinical settings, a high-quality treatment regime must balance multiple competing outcomes; eg, when a high dose is associated with substantial symptom reduction but a greater risk of an adverse event. We consider the problem of estimating the most efficacious treatment regime subject to constraints on the risk of adverse events. We combine nonparametric Q-learning with policy-search to estimate a high-quality yet parsimonious treatment regime. This estimator applies to both observational and randomized data, as well as settings with variable, outcome-dependent follow-up, mixed treatment types, and multiple time points. This work is motivated by and framed in the context of dosing for chronic pain; however, the proposed framework can be applied generally to estimate a treatment regime which maximizes the mean of one primary outcome subject to constraints on one or more secondary outcomes. We illustrate the proposed method using data pooled from 5 open-label flexible dosing clinical trials for chronic pain.
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Hibbard JC, Friedstat JS, Thomas SM, Edkins RE, Hultman CS, Kosorok MR. LIBERTI: A SMART study in plastic surgery. Clin Trials 2018; 15:286-293. [PMID: 29577741 DOI: 10.1177/1740774518762435] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND/AIMS Laser treatment of burns scars is considered by some providers to be standard of care. However, there is little evidence-based research as to the true benefit. A number of factors hinder evaluation of the benefit of laser treatment. These include significant heterogeneity in patient response and possible delayed effects from the laser treatment. Moreover, laser treatments are often provided sequentially using different types of equipment and settings, so there are effectively a large number of overall treatment options that need to be compared. We propose a trial capable of coping with these issues and that also attempts to take advantage of the heterogeneous response in order to estimate optimal treatment plans personalized to each individual patient. It will be the first large-scale randomized trial to compare the effectiveness of laser treatments for burns scars and, to our knowledge, the very first example of the utility of a Sequential Multiple Assignment Randomized Trial in plastic surgery. METHODS We propose using a Sequential Multiple Assignment Randomized Trial design to investigate the effect of various permutations of laser treatment on hypertrophic burn scars. We will compare and test hypotheses regarding laser treatment effects at a general population level. Simultaneously, we hope to use the data generated to discover possible beneficial personalized treatment plans, tailored to individual patient characteristics. RESULTS We show that the proposed trial has good power to detect laser treatment effect at the overall population level, despite comparing a large number of treatment combinations. The trial will simultaneously provide high-quality data appropriate for estimating precision-medicine treatment rules. We detail population-level comparisons of interest and corresponding sample size calculations. We provide simulations to suggest the power of the trial to detect laser effect and also the possible benefits of personalization of laser treatment to individual characteristics. CONCLUSION We propose, to our knowledge, the first use of a Sequential Multiple Assignment Randomized Trial in surgery. The trial is rigorously designed so that it is reasonably straightforward to implement and powered to answer general overall questions of interest. The trial is also designed to provide data that are suitable for the estimation of beneficial precision-medicine treatment rules that depend both on individual patient characteristics and on-going real-time patient response to treatment.
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Affiliation(s)
- Jonathan C Hibbard
- 1 Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,2 School of Mathematics, Institute for Advanced Study, Princeton, NJ, USA
| | - Jonathan S Friedstat
- 3 Division of Burns, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Renee E Edkins
- 5 Division of Plastic Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - C Scott Hultman
- 5 Division of Plastic Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michael R Kosorok
- 1 Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Chambaz A, Zheng W, van der Laan MJ. TARGETED SEQUENTIAL DESIGN FOR TARGETED LEARNING INFERENCE OF THE OPTIMAL TREATMENT RULE AND ITS MEAN REWARD. Ann Stat 2017; 45:2537-2564. [PMID: 29398733 PMCID: PMC5794253 DOI: 10.1214/16-aos1534] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
This article studies the targeted sequential inference of an optimal treatment rule (TR) and its mean reward in the non-exceptional case, i.e., assuming that there is no stratum of the baseline covariates where treatment is neither beneficial nor harmful, and under a companion margin assumption. Our pivotal estimator, whose definition hinges on the targeted minimum loss estimation (TMLE) principle, actually infers the mean reward under the current estimate of the optimal TR. This data-adaptive statistical parameter is worthy of interest on its own. Our main result is a central limit theorem which enables the construction of confidence intervals on both mean rewards under the current estimate of the optimal TR and under the optimal TR itself. The asymptotic variance of the estimator takes the form of the variance of an efficient influence curve at a limiting distribution, allowing to discuss the efficiency of inference. As a by product, we also derive confidence intervals on two cumulated pseudo-regrets, a key notion in the study of bandits problems. A simulation study illustrates the procedure. One of the corner-stones of the theoretical study is a new maximal inequality for martingales with respect to the uniform entropy integral.
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Affiliation(s)
- Antoine Chambaz
- UPL, Université Paris Nanterre
- University of California, Berkeley
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Cui Y, Zhu R, Kosorok M. Tree based weighted learning for estimating individualized treatment rules with censored data. Electron J Stat 2017; 11:3927-3953. [PMID: 29403568 PMCID: PMC5796682 DOI: 10.1214/17-ejs1305] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Estimating individualized treatment rules is a central task for personalized medicine. [23] and [22] proposed outcome weighted learning to estimate individualized treatment rules directly through maximizing the expected outcome without modeling the response directly. In this paper, we extend the outcome weighted learning to right censored survival data without requiring either inverse probability of censoring weighting or semiparametric modeling of the censoring and failure times as done in [26]. To accomplish this, we take advantage of the tree based approach proposed in [28] to nonparametrically impute the survival time in two different ways. The first approach replaces the reward of each individual by the expected survival time, while in the second approach only the censored observations are imputed by their conditional expected failure times. We establish consistency and convergence rates for both estimators. In simulation studies, our estimators demonstrate improved performance compared to existing methods. We also illustrate the proposed method on a phase III clinical trial of non-small cell lung cancer.
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Affiliation(s)
- Yifan Cui
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ruoqing Zhu
- Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
| | - Michael Kosorok
- Department of Biostatistics and Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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Wang L, Lin Y, Chen JT. Simultaneous inference for treatment regimes. COMMUN STAT-THEOR M 2017. [DOI: 10.1080/03610926.2016.1217017] [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)
- Lu Wang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Yong Lin
- Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, OH USA
| | - John T. Chen
- Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, OH USA
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Lizotte DJ, Tahmasebi A. Prediction and tolerance intervals for dynamic treatment regimes. Stat Methods Med Res 2017; 26:1611-1629. [PMID: 28695763 DOI: 10.1177/0962280217708662] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
We develop and evaluate tolerance interval methods for dynamic treatment regimes (DTRs) that can provide more detailed prognostic information to patients who will follow an estimated optimal regime. Although the problem of constructing confidence intervals for DTRs has been extensively studied, prediction and tolerance intervals have received little attention. We begin by reviewing in detail different interval estimation and prediction methods and then adapting them to the DTR setting. We illustrate some of the challenges associated with tolerance interval estimation stemming from the fact that we do not typically have data that were generated from the estimated optimal regime. We give an extensive empirical evaluation of the methods and discussed several practical aspects of method choice, and we present an example application using data from a clinical trial. Finally, we discuss future directions within this important emerging area of DTR research.
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Affiliation(s)
- Daniel J Lizotte
- Departments of Computer Science and Epidemiology & Biostatistics, The University of Western Ontario, London, Ontario, Canada
| | - Arezoo Tahmasebi
- Departments of Computer Science and Epidemiology & Biostatistics, The University of Western Ontario, London, Ontario, Canada
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Laber EB, Staicu AM. Functional feature construction for individualized treatment regimes. J Am Stat Assoc 2017; 113:1219-1227. [PMID: 30416232 PMCID: PMC6223315 DOI: 10.1080/01621459.2017.1321545] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2015] [Revised: 01/01/2017] [Indexed: 10/19/2022]
Abstract
Evidence-based personalized medicine formalizes treatment selection as an individualized treatment regime that maps up-to-date patient information into the space of possible treatments. Available patient information may include static features such race, gender, family history, genetic and genomic information, as well as longitudinal information including the emergence of comorbidities, waxing and waning of symptoms, side-effect burden, and adherence. Dynamic information measured at multiple time points before treatment assignment should be included as input to the treatment regime. However, subject longitudinal measurements are typically sparse, irregularly spaced, noisy, and vary in number across subjects. Existing estimators for treatment regimes require equal information be measured on each subject and thus standard practice is to summarize longitudinal subject information into a scalar, ad hoc summary during data pre-processing. This reduction of the longitudinal information to a scalar feature precedes estimation of a treatment regime and is therefore not informed by subject outcomes, treatments, or covariates. Furthermore, we show that this reduction requires more stringent causal assumptions for consistent estimation than are necessary. We propose a data-driven method for constructing maximally prescriptive yet interpretable features that can be used with standard methods for estimating optimal treatment regimes. In our proposed framework, we treat the subject longitudinal information as a realization of a stochastic process observed with error at discrete time points. Functionals of this latent process are then combined with outcome models to estimate an optimal treatment regime. The proposed methodology requires weaker causal assumptions than Q-learning with an ad hoc scalar summary and is consistent for the optimal treatment regime.
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Affiliation(s)
- Eric B Laber
- Department of Statistics, North Carolina State University, Raleigh, NC, 27695, U.S.A
| | - Ana-Maria Staicu
- Department of Statistics, North Carolina State University, Raleigh, NC, 27695, U.S.A
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Petkova E, Ogden RT, Tarpey T, Ciarleglio A, Jiang B, Su Z, Carmody T, Adams P, Kraemer HC, Grannemann BD, Oquendo MA, Parsey R, Weissman M, McGrath PJ, Fava M, Trivedi MH. Statistical Analysis Plan for Stage 1 EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care) Study. Contemp Clin Trials Commun 2017; 6:22-30. [PMID: 28670629 PMCID: PMC5485858 DOI: 10.1016/j.conctc.2017.02.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2016] [Revised: 02/08/2017] [Accepted: 02/13/2017] [Indexed: 12/28/2022] Open
Abstract
Antidepressant medications are commonly used to treat depression, but only about 30% of patients reach remission with any single first-step antidepressant. If the first-step treatment fails, response and remission rates at subsequent steps are even more limited. The literature on biomarkers for treatment response is largely based on secondary analyses of studies designed to answer primary questions of efficacy, rather than on a planned systematic evaluation of biomarkers for treatment decision. The lack of evidence-based knowledge to guide treatment decisions for patients with depression has lead to the recognition that specially designed studies with the primary objective being to discover biosignatures for optimizing treatment decisions are necessary. Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) is one such discovery study. Stage 1 of EMBARC is a randomized placebo controlled clinical trial of 8 week duration. A wide array of patient characteristics is collected at baseline, including assessments of brain structure, function and connectivity along with electrophysiological, biological, behavioral and clinical features. This paper reports on the data analytic strategy for discovering biosignatures for treatment response based on Stage 1 of EMBARC.
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Affiliation(s)
- Eva Petkova
- New York University, New York, NY, USA
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | | | - Thaddeus Tarpey
- New York University, New York, NY, USA
- Wright State University, Dayton, OH, USA
| | - Adam Ciarleglio
- New York University, New York, NY, USA
- Columbia University, New York, NY, USA
| | - Bei Jiang
- University of Alberta, Edmonton, Alberta, Canada
| | - Zhe Su
- New York University, New York, NY, USA
| | - Thomas Carmody
- University of Texas, Southwestern Medical Center, Dallas, TX, USA
| | - Philip Adams
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
| | | | | | - Maria A. Oquendo
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
| | | | - Myrna Weissman
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
| | - Patrick J. McGrath
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
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Kelleher SA, Dorfman CS, Plumb Vilardaga JC, Majestic C, Winger J, Gandhi V, Nunez C, Van Denburg A, Shelby RA, Reed SD, Murphy S, Davidian M, Laber EB, Kimmick GG, Westbrook KW, Abernethy AP, Somers TJ. Optimizing delivery of a behavioral pain intervention in cancer patients using a sequential multiple assignment randomized trial SMART. Contemp Clin Trials 2017; 57:51-57. [PMID: 28408335 DOI: 10.1016/j.cct.2017.04.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2016] [Revised: 03/21/2017] [Accepted: 04/08/2017] [Indexed: 01/25/2023]
Abstract
BACKGROUND/AIMS Pain is common in cancer patients and results in lower quality of life, depression, poor physical functioning, financial difficulty, and decreased survival time. Behavioral pain interventions are effective and nonpharmacologic. Traditional randomized controlled trials (RCT) test interventions of fixed time and dose, which poorly represent successive treatment decisions in clinical practice. We utilize a novel approach to conduct a RCT, the sequential multiple assignment randomized trial (SMART) design, to provide comparative evidence of: 1) response to differing initial doses of a pain coping skills training (PCST) intervention and 2) intervention dose sequences adjusted based on patient response. We also examine: 3) participant characteristics moderating intervention responses and 4) cost-effectiveness and practicality. METHODS/DESIGN Breast cancer patients (N=327) having pain (ratings≥5) are recruited and randomly assigned to: 1) PCST-Full or 2) PCST-Brief. PCST-Full consists of 5 PCST sessions. PCST-Brief consists of one 60-min PCST session. Five weeks post-randomization, participants re-rate their pain and are re-randomized, based on intervention response, to receive additional PCST sessions, maintenance calls, or no further intervention. Participants complete measures of pain intensity, interference and catastrophizing. CONCLUSIONS Novel RCT designs may provide information that can be used to optimize behavioral pain interventions to be adaptive, better meet patients' needs, reduce barriers, and match with clinical practice. This is one of the first trials to use a novel design to evaluate symptom management in cancer patients and in chronic illness; if successful, it could serve as a model for future work with a wide range of chronic illnesses.
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Affiliation(s)
- Sarah A Kelleher
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, United States
| | - Caroline S Dorfman
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, United States
| | - Jen C Plumb Vilardaga
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, United States
| | - Catherine Majestic
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, United States
| | - Joseph Winger
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, United States
| | - Vicky Gandhi
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, United States
| | - Christine Nunez
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, United States
| | - Alyssa Van Denburg
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, United States
| | - Rebecca A Shelby
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, United States
| | - Shelby D Reed
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, United States
| | - Susan Murphy
- Department of Statistics, University of Michigan, Ann Arbor, MI, United States
| | - Marie Davidian
- Department of Statistics, North Carolina State University, Raleigh, NC, United States
| | - Eric B Laber
- Department of Statistics, North Carolina State University, Raleigh, NC, United States
| | - Gretchen G Kimmick
- Department of Internal Medicine, Duke Cancer Institute, Duke University Medical Center, Durham, NC, United States
| | - Kelly W Westbrook
- Department of Internal Medicine, Duke Cancer Institute, Duke University Medical Center, Durham, NC, United States
| | - Amy P Abernethy
- Division of Medical Oncology, Duke University Medical Center, Durham, NC, United States
| | - Tamara J Somers
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, United States.
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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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Chen G, Zeng D, Kosorok MR. Rejoinder. J Am Stat Assoc 2017. [DOI: 10.1080/01621459.2016.1250573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Guanhua Chen
- Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina, Chapel, Hill, NC, USA
| | - Michael R. Kosorok
- Department of Biostatistics, and Professor, Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, USA
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Lizotte DJ, Laber EB. Multi-Objective Markov Decision Processes for Data-Driven Decision Support. JOURNAL OF MACHINE LEARNING RESEARCH : JMLR 2016; 17:211. [PMID: 28018133 PMCID: PMC5179144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
We present new methodology based on Multi-Objective Markov Decision Processes for developing sequential decision support systems from data. Our approach uses sequential decision-making data to provide support that is useful to many different decision-makers, each with different, potentially time-varying preference. To accomplish this, we develop an extension of fitted-Q iteration for multiple objectives that computes policies for all scalarization functions, i.e. preference functions, simultaneously from continuous-state, finite-horizon data. We identify and address several conceptual and computational challenges along the way, and we introduce a new solution concept that is appropriate when different actions have similar expected outcomes. Finally, we demonstrate an application of our method using data from the Clinical Antipsychotic Trials of Intervention Effectiveness and show that our approach offers decision-makers increased choice by a larger class of optimal policies.
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Affiliation(s)
- Daniel J Lizotte
- Department of Computer Science, Department of Epidemiology & Biostatistics, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 3K7, Canada
| | - Eric B Laber
- Department of Statistics, North Carolina State University, Raliegh, NC 27695, USA
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