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Wei K, Qin G, Zhu Z. Subgroup analysis for longitudinal data based on a partial linear varying coefficient model with a change plane. Stat Med 2023; 42:3716-3731. [PMID: 37314008 DOI: 10.1002/sim.9827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 05/01/2023] [Accepted: 05/31/2023] [Indexed: 06/15/2023]
Abstract
Subgroup analysis has become an important tool to characterize the treatment effect heterogeneity, and finally towards precision medicine. On the other hand, longitudinal study is widespread in many fields, but subgroup analysis for this data type is still limited. In this article, we study a partial linear varying coefficient model with a change plane, in which the subgroups are defined based on linear combination of grouping variables, and the time-varying effects in different subgroups are estimated to capture the dynamic association between predictors and response. The varying coefficients are approximated by basis functions and the group indicator function is smoothed by kernel function, which are included in the generalized estimating equation for estimation. Asymptotic properties of the estimators for the varying coefficients, the constant coefficients and the change plane coefficients are established. Simulations are conducted to demonstrate the flexibility, efficiency and robustness of the proposed method. Based on the Standard and New Antiepileptic Drugs study, we successfully identify a subgroup in which patients are sensitive to the newer drug in a specific period of time.
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Affiliation(s)
- Kecheng Wei
- Department of Biostatistics, Key Laboratory for Health Technology Assessment, National Commission of Health, Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai, China
| | - Guoyou Qin
- Department of Biostatistics, Key Laboratory for Health Technology Assessment, National Commission of Health, Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai, China
| | - Zhongyi Zhu
- Department of Statistics, School of Management, Fudan University, Shanghai, China
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Jin P, Lu W, Chen Y, Liu M. Change-plane analysis for subgroup detection with a continuous treatment. Biometrics 2023; 79:1920-1933. [PMID: 36134534 PMCID: PMC10030385 DOI: 10.1111/biom.13762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 09/14/2022] [Indexed: 11/30/2022]
Abstract
Detecting and characterizing subgroups with differential effects of a binary treatment has been widely studied and led to improvements in patient outcomes and population risk management. Under the setting of a continuous treatment, however, such investigations remain scarce. We propose a semiparametric change-plane model and consequently a doubly robust test statistic for assessing the existence of two subgroups with differential treatment effects under a continuous treatment. The proposed testing procedure is valid when either the baseline function for the covariate effects or the generalized propensity score function for the continuous treatment is correctly specified. The asymptotic distributions of the test statistic under the null and local alternative hypotheses are established. When the null hypothesis of no subgroup is rejected, the change-plane parameters that define the subgroups can be estimated. This paper provides a unified framework of the change-plane method to handle various types of outcomes, including the exponential family of distributions and time-to-event outcomes. Additional extensions with nonparametric estimation approaches are also provided. We evaluate the performance of our proposed methods through extensive simulation studies under various scenarios. An application to the Health Effects of Arsenic Longitudinal Study with a continuous environmental exposure of arsenic is presented.
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Affiliation(s)
- Peng Jin
- Division of Biostatistics, Department of Population Health, NYU Grossman School of Medicine, New York, New York 10016, U.S.A
| | - Wenbin Lu
- Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695, U.S.A
| | - Yu Chen
- Division of Epidemiplogy, Department of Population Health, NYU Grossman School of Medicine, New York, New York 10016, U.S.A
- Department of Environmental Medicine, NYU Grossman School of Medicine, New York, New York 10016, U.S.A
| | - Mengling Liu
- Division of Biostatistics, Department of Population Health, NYU Grossman School of Medicine, New York, New York 10016, U.S.A
- Department of Environmental Medicine, NYU Grossman School of Medicine, New York, New York 10016, U.S.A
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3
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Liu P, Li J, Kosorok MR. Change plane model averaging for subgroup identification. Stat Methods Med Res 2023; 32:773-788. [PMID: 36775991 DOI: 10.1177/09622802231154327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
Abstract
Central to personalized medicine and tailored therapies is discovering the subpopulations that account for treatment effect heterogeneity and are likely to benefit more from given interventions. In this article, we introduce a change plane model averaging method to identify subgroups characterized by linear combinations of predictive variables and multiple cut-offs. We first fit a sequence of statistical models, each incorporating the thresholding effect of one particular covariate. The estimation of submodels is accomplished through an iterative integration of a change point detection method and numerical optimization algorithms. A frequentist model averaging approach is then employed to linearly combine the submodels with optimal weights. Our approach can deal with high-dimensional settings involving enormous potential grouping variables by adopting the sparsity-inducing penalties. Simulation studies are conducted to investigate the prediction and subgrouping performance of the proposed method, with a comparison to various competing subgroup detection methods. Our method is applied to a dataset from a warfarin pharmacogenetics study, producing some new findings.
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Affiliation(s)
- Pan Liu
- Department of Statistics and Data Science, 37580National University of Singapore, Singapore, Singapore
| | - Jialiang Li
- Department of Statistics and Data Science, 37580National University of Singapore, Singapore, Singapore.,Duke University NUS Graduate Medical School, Singapore, Singapore
| | - Michael R Kosorok
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, USA
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Liu P, Li J. Segment regression model average with multiple threshold variables and multiple structural breaks. CAN J STAT 2023. [DOI: 10.1002/cjs.11764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Pan Liu
- Department of Statistics and Applied Probability National University of Singapore Singapore
| | - Jialiang Li
- Department of Statistics and Applied Probability National University of Singapore Singapore
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Zhou J, Zhang Y, Tu W. A reference-free R-learner for treatment recommendation. Stat Methods Med Res 2023; 32:404-424. [PMID: 36540907 DOI: 10.1177/09622802221144326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Assigning optimal treatments to individual patients based on their characteristics is the ultimate goal of precision medicine. Deriving evidence-based recommendations from observational data while considering the causal treatment effects and patient heterogeneity is a challenging task, especially in situations of multiple treatment options. Herein, we propose a reference-free R-learner based on a simplex algorithm for treatment recommendation. We showed through extensive simulation that the proposed method produced accurate recommendations that corresponded to optimal treatment outcomes, regardless of the reference group. We used the method to analyze data from the Systolic Blood Pressure Intervention Trial (SPRINT) and achieved recommendations consistent with the current clinical guidelines.
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Affiliation(s)
- Junyi Zhou
- Design and Inovation, 7129Amgen Inc., Thousand Oaks, CA, USA
| | - Ying Zhang
- Department of Biostatistics, 12284University of Nebraska Medical Center, Omaha, NE, USA
| | - Wanzhu Tu
- Department of Biostatistics and Health Data Science, Indiana University-School of Medicine and Fairbanks School of Public Health, Indianapolis, IN, USA
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Li N, Song Y, Lin CD, Tu D. Bootstrap adjusted predictive classification for identification of subgroups with differential treatment effects under generalized linear models. Electron J Stat 2023. [DOI: 10.1214/23-ejs2108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Na Li
- Department of Mathematics and Statistics, Queen’s University, Kingston, ON, K7L 3N6, Canada
| | - Yanglei Song
- Department of Mathematics and Statistics, Queen’s University, Kingston, ON, K7L 3N6, Canada
| | - C. Devon Lin
- Department of Mathematics and Statistics, Queen’s University, Kingston, ON, K7L 3N6, Canada
| | - Dongsheng Tu
- Canadian Cancer Trials Group, Departments of Public Health Sciences & Mathematics and Statistics, Queen’s University, Kingston, ON, K7L 3N6, Canada
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Wei K, Zhu H, Qin G, Zhu Z, Tu D. Multiply robust subgroup analysis based on a single-index threshold linear marginal model for longitudinal data with dropouts. Stat Med 2022; 41:2822-2839. [PMID: 35347738 DOI: 10.1002/sim.9386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 02/21/2022] [Accepted: 03/02/2022] [Indexed: 11/08/2022]
Abstract
Identifying subpopulations that may be sensitive to the specific treatment is an important step toward precision medicine. On the other hand, longitudinal data with dropouts is common in medical research, and subgroup analysis for this data type is still limited. In this paper, we consider a single-index threshold linear marginal model, which can be used simultaneously to identify subgroups with differential treatment effects based on linear combination of the selected biomarkers, estimate the treatment effects in different subgroups based on regression coefficients, and test the significance of the difference in treatment effects based on treatment-subgroup interaction. The regression parameters are estimated by solving a penalized smoothed generalized estimating equation and the selection bias caused by missingness is corrected by a multiply robust weighting matrix, which allows multiple missingness models to be taken account into estimation. The proposed estimator remains consistent when any model for missingness is correctly specified. Under regularity conditions, the asymptotic normality of the estimator is established. Simulation studies confirm the desirable finite-sample performance of the proposed method. As an application, we analyze the data from a clinical trial on pancreatic cancer.
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Affiliation(s)
- Kecheng Wei
- Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China
| | - Huichen Zhu
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China
| | - Guoyou Qin
- Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China
| | - Zhongyi Zhu
- Department of Statistics, School of Management, Fudan University, Shanghai, China
| | - Dongsheng Tu
- Canadian Cancer Trials Group, Queen's University, Kingston, Ontario, Canada
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Kulasekera KB, Tholkage S, Kong M. Personalized treatment selection using observational data. J Appl Stat 2022; 50:1115-1127. [PMID: 37009593 PMCID: PMC10062224 DOI: 10.1080/02664763.2021.2019689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Estimating the optimal treatment regime based on individual patient characteristics has been a topic of discussion in many forums. Advanced computational power has added momentum to this discussion over the last two decades and practitioners have been advocating the use of new methods in determining the best treatment. Treatments that are geared toward the 'best' outcome for a patient based on his/her genetic markers and characteristics are of high importance. In this article, we develop an approach to predict the optimal personalized treatment based on observational data. We have used inverse probability of treatment weighted machine learning methods to obtain score functions to predict the optimal treatment. Extensive simulation studies showed that our proposed method has desirable performance in selecting the optimal treatment. We provided a case study to examine the Statin use on cognitive function to illustrate the use of our proposed method.
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Affiliation(s)
- K. B. Kulasekera
- Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY, USA
| | - Sudaraka Tholkage
- Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY, USA
| | - Maiying Kong
- Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY, USA
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