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Rhodes G, Davidian M, Lu W. Estimation of optimal treatment regimes with electronic medical record data using the residual life value estimator. Biostatistics 2024:kxae002. [PMID: 38332633 DOI: 10.1093/biostatistics/kxae002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 11/10/2023] [Accepted: 01/03/2024] [Indexed: 02/10/2024] Open
Abstract
Clinicians and patients must make treatment decisions at a series of key decision points throughout disease progression. A dynamic treatment regime is a set of sequential decision rules that return treatment decisions based on accumulating patient information, like that commonly found in electronic medical record (EMR) data. When applied to a patient population, an optimal treatment regime leads to the most favorable outcome on average. Identifying optimal treatment regimes that maximize residual life is especially desirable for patients with life-threatening diseases such as sepsis, a complex medical condition that involves severe infections with organ dysfunction. We introduce the residual life value estimator (ReLiVE), an estimator for the expected value of cumulative restricted residual life under a fixed treatment regime. Building on ReLiVE, we present a method for estimating an optimal treatment regime that maximizes expected cumulative restricted residual life. Our proposed method, ReLiVE-Q, conducts estimation via the backward induction algorithm Q-learning. We illustrate the utility of ReLiVE-Q in simulation studies, and we apply ReLiVE-Q to estimate an optimal treatment regime for septic patients in the intensive care unit using EMR data from the Multiparameter Intelligent Monitoring Intensive Care database. Ultimately, we demonstrate that ReLiVE-Q leverages accumulating patient information to estimate personalized treatment regimes that optimize a clinically meaningful function of residual life.
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Affiliation(s)
- Grace Rhodes
- Eli Lilly and Company, Indianapolis, IN 46204, USA
| | - Marie Davidian
- Department of Statistics, North Carolina State University, SAS Hall, 2311 Stinson Dr, Raleigh, NC 27607, USA
| | - Wenbin Lu
- Department of Statistics, North Carolina State University, SAS Hall, 2311 Stinson Dr, Raleigh, NC 27607, USA
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Dong X, Zheng Y, Lin DW, Newcomb L, Zhao YQ. Constructing time-invariant dynamic surveillance rules for optimal monitoring schedules. Biometrics 2023; 79:3895-3906. [PMID: 37479875 PMCID: PMC10866138 DOI: 10.1111/biom.13911] [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: 03/22/2022] [Accepted: 05/22/2023] [Indexed: 07/23/2023]
Abstract
Dynamic surveillance rules (DSRs) are sequential surveillance decision rules informing monitoring schedules in clinical practice, which can adapt over time according to a patient's evolving characteristics. In many clinical applications, it is desirable to identify and implement optimal time-invariant DSRs, where the parameters indexing the decision rules are shared across different decision points. We propose a new criterion for DSRs that accounts for benefit-cost tradeoff during the course of disease surveillance. We develop two methods to estimate the time-invariant DSRs optimizing the proposed criterion, and establish asymptotic properties for the estimated parameters of biomarkers indexing the DSRs. The first approach estimates the optimal decision rules for each individual at every stage via regression modeling, and then estimates the time-invariant DSRs via a classification procedure with the estimated time-varying decision rules as the response. The second approach proceeds by optimizing a relaxation of the empirical objective, where a surrogate function is utilized to facilitate computation. Extensive simulation studies are conducted to demonstrate the superior performances of the proposed methods. The methods are further applied to the Canary Prostate Active Surveillance Study (PASS).
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Affiliation(s)
| | - Yingye Zheng
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, U.S.A
| | - Daniel W. Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, U.S.A
- Department of Urology, University of Washington, Seattle, Washington, U.S.A
| | - Lisa Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, U.S.A
- Department of Urology, University of Washington, Seattle, Washington, U.S.A
| | - Ying-Qi Zhao
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, U.S.A
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Accountable survival contrast-learning for optimal dynamic treatment regimes. Sci Rep 2023; 13:2250. [PMID: 36755137 PMCID: PMC9908913 DOI: 10.1038/s41598-023-29106-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 01/30/2023] [Indexed: 02/10/2023] Open
Abstract
Dynamic treatment regime (DTR) is an emerging paradigm in recent medical studies, which searches a series of decision rules to assign optimal treatments to each patient by taking into account individual features such as genetic, environmental, and social factors. Although there is a large and growing literature on statistical methods to estimate optimal treatment regimes, most methodologies focused on complete data. In this article, we propose an accountable contrast-learning algorithm for optimal dynamic treatment regime with survival endpoints. Our estimating procedure is originated from a doubly-robust weighted classification scheme, which is a model-based contrast-learning method that directly characterizes the interaction terms between predictors and treatments without main effects. To reflect the censorship, we adopt the pseudo-value approach that replaces survival quantities with pseudo-observations for the time-to-event outcome. Unlike many existing approaches, mostly based on complicated outcome regression modeling or inverse-probability weighting schemes, the pseudo-value approach greatly simplifies the estimating procedure for optimal treatment regime by allowing investigators to conveniently apply standard machine learning techniques to censored survival data without losing much efficiency. We further explore a SCAD-penalization to find informative clinical variables and modified algorithms to handle multiple treatment options by searching upper and lower bounds of the objective function. We demonstrate the utility of our proposal via extensive simulations and application to AIDS data.
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Zhang Z, Yi D, Fan Y. Doubly robust estimation of optimal dynamic treatment regimes with multicategory treatments and survival outcomes. Stat Med 2022; 41:4903-4923. [PMID: 35948279 DOI: 10.1002/sim.9543] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 05/31/2022] [Accepted: 07/21/2022] [Indexed: 11/06/2022]
Abstract
Patients with chronic diseases, such as cancer or epilepsy, are often followed through multiple stages of clinical interventions. Dynamic treatment regimes (DTRs) are sequences of decision rules that assign treatments at each stage based on measured covariates for each patient. A DTR is said to be optimal if the expectation of the desirable clinical benefit reaches a maximum when applied to a population. When there are three or more options for treatments at each decision point and the clinical outcome of interest is a time-to-event variable, estimating an optimal DTR can be complicated. We propose a doubly robust method to estimate optimal DTRs with multicategory treatments and survival outcomes. A novel blip function is defined to measure the difference in expected outcomes among treatments, and a doubly robust weighted least squares algorithm is designed for parameter estimation. Simulations using various weight functions and scenarios support the advantages of the proposed method in estimating optimal DTRs over existing approaches. We further illustrate the practical value of our method by applying it to data from the Standard and New Antiepileptic Drugs study. In this analysis, the proposed method supports the use of the new drug lamotrigine over the standard option carbamazepine. When the actual treatments match the estimated optimal treatments, survival outcomes tend to be better. The newly developed method provides a practical approach for clinicians that is not limited to cases of binary treatment options.
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Affiliation(s)
- Zhang Zhang
- Center for Applied Statistics, Renmin University of China, Beijing, China.,School of Statistics, Renmin University of China, Beijing, China
| | - Danhui Yi
- Center for Applied Statistics, Renmin University of China, Beijing, China.,School of Statistics, Renmin University of China, Beijing, China
| | - Yiwei Fan
- School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China
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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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Mahar RK, McGuinness MB, Chakraborty B, Carlin JB, IJzerman MJ, Simpson JA. A scoping review of studies using observational data to optimise dynamic treatment regimens. BMC Med Res Methodol 2021; 21:39. [PMID: 33618655 PMCID: PMC7898728 DOI: 10.1186/s12874-021-01211-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 01/19/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Dynamic treatment regimens (DTRs) formalise the multi-stage and dynamic decision problems that clinicians often face when treating chronic or progressive medical conditions. Compared to randomised controlled trials, using observational data to optimise DTRs may allow a wider range of treatments to be evaluated at a lower cost. This review aimed to provide an overview of how DTRs are optimised with observational data in practice. METHODS Using the PubMed database, a scoping review of studies in which DTRs were optimised using observational data was performed in October 2020. Data extracted from eligible articles included target medical condition, source and type of data, statistical methods, and translational relevance of the included studies. RESULTS From 209 PubMed abstracts, 37 full-text articles were identified, and a further 26 were screened from the reference lists, totalling 63 articles for inclusion in a narrative data synthesis. Observational DTR models are a recent development and their application has been concentrated in a few medical areas, primarily HIV/AIDS (27, 43%), followed by cancer (8, 13%), and diabetes (6, 10%). There was substantial variation in the scope, intent, complexity, and quality between the included studies. Statistical methods that were used included inverse-probability weighting (26, 41%), the parametric G-formula (16, 25%), Q-learning (10, 16%), G-estimation (4, 6%), targeted maximum likelihood/minimum loss-based estimation (4, 6%), regret regression (3, 5%), and other less common approaches (10, 16%). Notably, studies that were primarily intended to address real-world clinical questions (18, 29%) tended to use inverse-probability weighting and the parametric G-formula, relatively well-established methods, along with a large amount of data. Studies focused on methodological developments (45, 71%) tended to be more complicated and included a demonstrative real-world application only. CONCLUSIONS As chronic and progressive conditions become more common, the need will grow for personalised treatments and methods to estimate the effects of DTRs. Observational DTR studies will be necessary, but so far their use to inform clinical practice has been limited. Focusing on simple DTRs, collecting large and rich clinical datasets, and fostering tight partnerships between content experts and data analysts may result in more clinically relevant observational DTR studies.
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Affiliation(s)
- Robert K Mahar
- Biostatistics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.
- Cancer Health Services Research Unit, University of Melbourne Centre for Cancer Research and Centre for Health Policy, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.
- Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia.
| | - Myra B McGuinness
- Biostatistics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Victoria, Australia
| | - Bibhas Chakraborty
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
- Department of Statistics and Applied Probability, Faculty of Science, National University of Singapore, Singapore, Singapore
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA
| | - John B Carlin
- Biostatistics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Maarten J IJzerman
- Cancer Health Services Research Unit, University of Melbourne Centre for Cancer Research and Centre for Health Policy, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
- Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia
| | - Julie A Simpson
- Biostatistics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
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Wang S, Moodie EE, Stephens DA, Nijjar JS. Adaptive treatment strategies for chronic conditions: shared-parameter G-estimation with an application to rheumatoid arthritis. Biostatistics 2020; 23:kxaa033. [PMID: 32851395 DOI: 10.1093/biostatistics/kxaa033] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 07/15/2020] [Accepted: 07/17/2020] [Indexed: 11/13/2022] Open
Abstract
Most estimation algorithms for adaptive treatment strategies assume that treatment rules at each decision point are independent from one another in the sense that they do not possess any common parameters. This is often unrealistic, as the same decisions may be made repeatedly over time. Sharing treatment-decision parameters across decision points offers several advantages, including estimation of fewer parameters and the clinical ease of a single, time-invariant decision to implement. We propose a new computational approach to estimation of shared-parameter G-estimation, which is efficient and shares the double robustness of the "unshared" sequential G-estimation. We use this approach to analyze data from the Scottish Early Rheumatoid Arthritis (SERA) Inception Cohort.
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Affiliation(s)
- Shouao Wang
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC Canada, H3A 1A2
| | - Erica Em Moodie
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC Canada, H3A 1A2
| | - David A Stephens
- Department of Mathematics and Statistics, McGill University, Montreal, QC Canada, H3A 0B9
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