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Moodie EEM, Bian Z, Coulombe J, Lian Y, Yang AY, Shortreed SM. Variable selection in high dimensions for discrete-outcome individualized treatment rules: Reducing severity of depression symptoms. Biostatistics 2023:kxad022. [PMID: 37660312 DOI: 10.1093/biostatistics/kxad022] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 07/14/2023] [Accepted: 08/03/2023] [Indexed: 09/05/2023] Open
Abstract
Despite growing interest in estimating individualized treatment rules, little attention has been given the binary outcome setting. Estimation is challenging with nonlinear link functions, especially when variable selection is needed. We use a new computational approach to solve a recently proposed doubly robust regularized estimating equation to accomplish this difficult task in a case study of depression treatment. We demonstrate an application of this new approach in combination with a weighted and penalized estimating equation to this challenging binary outcome setting. We demonstrate the double robustness of the method and its effectiveness for variable selection. The work is motivated by and applied to an analysis of treatment for unipolar depression using a population of patients treated at Kaiser Permanente Washington.
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Affiliation(s)
- Erica E M Moodie
- McGill University, Department of Epidemiology & Biostatistics, 2001 McGill College Ave, Suite 1200, Montreal, QC Canada H3A 1G1
| | - Zeyu Bian
- McGill University, Department of Epidemiology & Biostatistics, 2001 McGill College Ave, Suite 1200, Montreal, QC Canada H3A 1G1
| | - Janie Coulombe
- Université de Montréal, Department of Mathematics & Statistics, Pavillon André-Aisenstadt, Montréal, QC Canada H3C 3J7
| | - Yi Lian
- McGill University, Department of Epidemiology & Biostatistics, 2001 McGill College Ave, Suite 1200, Montreal, QC Canada H3A 1G1
| | - Archer Y Yang
- McGill University, Department of Mathematics & Statistics, 805 Sherbrooke Street West Montreal, QC Canada H3A 0B9
| | - Susan M Shortreed
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Suite 1600, Seattle, WA 98101
- University of Washington, Department of Biostatistics, 1705 NE Pacific St, Seattle, WA 98195
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Coulombe J, Moodie EEM, Shortreed SM, Renoux C. Estimating individualized treatment rules in longitudinal studies with covariate-driven observation times. Stat Methods Med Res 2023; 32:868-884. [PMID: 36927216 PMCID: PMC10248307 DOI: 10.1177/09622802231158733] [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] [Indexed: 03/18/2023]
Abstract
The sequential treatment decisions made by physicians to treat chronic diseases are formalized in the statistical literature as dynamic treatment regimes. To date, methods for dynamic treatment regimes have been developed under the assumption that observation times, that is, treatment and outcome monitoring times, are determined by study investigators. That assumption is often not satisfied in electronic health records data in which the outcome, the observation times, and the treatment mechanism are associated with patients' characteristics. The treatment and observation processes can lead to spurious associations between the treatment of interest and the outcome to be optimized under the dynamic treatment regime if not adequately considered in the analysis. We address these associations by incorporating two inverse weights that are functions of a patient's covariates into dynamic weighted ordinary least squares to develop optimal single stage dynamic treatment regimes, known as individualized treatment rules. We show empirically that our methodology yields consistent, multiply robust estimators. In a cohort of new users of antidepressant drugs from the United Kingdom's Clinical Practice Research Datalink, the proposed method is used to develop an optimal treatment rule that chooses between two antidepressants to optimize a utility function related to the change in body mass index.
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Affiliation(s)
- Janie Coulombe
- Department of Mathematics and
Statistics, Université de Montréal, Montreal, Canada
| | - Erica EM Moodie
- Department of Epidemiology,
Biostatistics and Occupational Health, McGill University, Montreal, Canada
| | - Susan M Shortreed
- Biostatistics Unit, Kaiser Permanente Washington Health
Research Institute, Seattle, Washington, USA
- Biostatistics Department, University of Washington, Seattle, Washington, USA
| | - Christel Renoux
- Lady Davis Institute for Medical
Research, Jewish General Hospital, Montreal, Canada
- Department of Neurology and
Neurosurgery, McGill University, Montreal, Canada
- Department of Epidemiology,
Biostatistics and Occupational Health, Mcgill University, Montreal, Canada
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Cai H, Bai W, Du X, Zhang L, Zhang L, Li YC, Liu HZ, Tang YL, Jackson T, Cheung T, An FR, Xiang YT. COVID-19 vaccine acceptance and perceived stigma in patients with depression: a network perspective. Transl Psychiatry 2022; 12:429. [PMID: 36195590 PMCID: PMC9530420 DOI: 10.1038/s41398-022-02170-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 09/06/2022] [Accepted: 09/09/2022] [Indexed: 11/20/2022] Open
Abstract
The association between coronavirus disease (COVID-19) vaccine acceptance and perceived stigma of having a mental illness is not clear. This study examined the association between COVID-19 vaccine acceptance and perceived stigma among patients with recurrent depressive disorder (depression hereafter) using network analysis. Participants were 1149 depressed patients (842 men, 307 women) who completed survey measures of perceived stigma and COVID-19 vaccine attitudes. T-tests, chi-square tests, and Kruskal-Wallis tests were used to compare differences in demographic and clinical characteristics between depressed patients who indented to accepted vaccines and those who were hesitant. Hierarchical multiple regression analyses assessed the unique association between COVID-19 vaccine acceptance and perceived stigma, independent of depression severity. Network analysis examined item-level relations between COVID-19 vaccine acceptance and perceived stigma after controlling for depressive symptoms. Altogether, 617 depressed patients (53.7%, 95 confidence intervals (CI) %: 50.82-56.58%) reported they would accept future COVID-19 vaccination. Hierarchical multiple regression analyses indicated higher perceived stigma scores predicted lower levels of COVID-19 vaccination acceptance (β = -0.125, P < 0.001), even after controlling for depression severity. In the network model of COVID-19 vaccination acceptance and perceived stigma nodes, "Feel others avoid me because of my illness", "Feel useless", and "Feel less competent than I did before" were the most influential symptoms. Furthermore, "COVID-19 vaccination acceptance" had the strongest connections with illness stigma items reflecting social rejection or social isolation concerns ("Employers/co-workers have discriminated", "Treated with less respect than usual", "Sense of being unequal in my relationships with others"). Given that a substantial proportion of depressed patients reported hesitancy with accepting COVID-19 vaccines and experiences of mental illness stigma related to social rejection and social isolation, providers working with this group should provide interventions to reduce stigma concerns toward addressing reluctance in receiving COVID-19 vaccines.
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Affiliation(s)
- Hong Cai
- grid.437123.00000 0004 1794 8068Unit of Psychiatry, Department of Public Health and Medicinal Administration, & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao, Macao SAR China ,grid.437123.00000 0004 1794 8068Centre for Cognitive and Brain Sciences, University of Macau, Macao, Macao SAR China ,grid.437123.00000 0004 1794 8068Institute of Advanced Studies in Humanities and Social Sciences, University of Macau, Macao, Macao SAR China
| | - Wei Bai
- grid.437123.00000 0004 1794 8068Unit of Psychiatry, Department of Public Health and Medicinal Administration, & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao, Macao SAR China ,grid.437123.00000 0004 1794 8068Centre for Cognitive and Brain Sciences, University of Macau, Macao, Macao SAR China ,grid.437123.00000 0004 1794 8068Institute of Advanced Studies in Humanities and Social Sciences, University of Macau, Macao, Macao SAR China
| | - Xiangdong Du
- grid.263761.70000 0001 0198 0694Guangji Hospital Affiliated to Soochow University, Suzhou, Jiangsu province China
| | - Ling Zhang
- Nanning Fifth People’s Hospital, Nanning, Guangxi province China
| | - Lan Zhang
- grid.411294.b0000 0004 1798 9345Department of Psychiatry, Lanzhou University Second Hospital, Lanzhou, Gansu province China
| | - Yu-Chen Li
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Huan-Zhong Liu
- grid.186775.a0000 0000 9490 772XDepartment of Psychiatry, Chaohu Hospital, Anhui Medical University, Hefei, China ,grid.186775.a0000 0000 9490 772XSchool of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
| | - Yi-Lang Tang
- grid.189967.80000 0001 0941 6502Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA USA ,grid.414026.50000 0004 0419 4084Atlanta VA Medical Center, Atlanta, GA USA
| | - Todd Jackson
- grid.437123.00000 0004 1794 8068Department of Psychology, University of Macau, Macao, Macao SAR China
| | - Teris Cheung
- grid.16890.360000 0004 1764 6123School of Nursing, Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR China
| | - Feng-Rong An
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Yu-Tao Xiang
- Unit of Psychiatry, Department of Public Health and Medicinal Administration, & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao, Macao SAR, China. .,Centre for Cognitive and Brain Sciences, University of Macau, Macao, Macao SAR, China. .,Institute of Advanced Studies in Humanities and Social Sciences, University of Macau, Macao, Macao SAR, China.
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Coulombe J, Moodie EEM, Platt RW, Renoux C. Estimation of the marginal effect of antidepressants on body mass index under confounding and endogenous covariate-driven monitoring times. Ann Appl Stat 2022. [DOI: 10.1214/21-aoas1570] [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: 11/19/2022]
Affiliation(s)
- Janie Coulombe
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University
| | - Erica E. M. Moodie
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University
| | - Robert W. Platt
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University
| | - Christel Renoux
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University
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Moodie EEM, Coulombe J, Danieli C, Renoux C, Shortreed SM. Privacy-preserving estimation of an optimal individualized treatment rule: a case study in maximizing time to severe depression-related outcomes. Lifetime Data Anal 2022; 28:512-542. [PMID: 35499604 PMCID: PMC10805063 DOI: 10.1007/s10985-022-09554-8] [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] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 03/08/2022] [Indexed: 06/14/2023]
Abstract
Estimating individualized treatment rules-particularly in the context of right-censored outcomes-is challenging because the treatment effect heterogeneity of interest is often small, thus difficult to detect. While this motivates the use of very large datasets such as those from multiple health systems or centres, data privacy may be of concern with participating data centres reluctant to share individual-level data. In this case study on the treatment of depression, we demonstrate an application of distributed regression for privacy protection used in combination with dynamic weighted survival modelling (DWSurv) to estimate an optimal individualized treatment rule whilst obscuring individual-level data. In simulations, we demonstrate the flexibility of this approach to address local treatment practices that may affect confounding, and show that DWSurv retains its double robustness even when performed through a (weighted) distributed regression approach. The work is motivated by, and illustrated with, an analysis of treatment for unipolar depression using the United Kingdom's Clinical Practice Research Datalink.
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Affiliation(s)
- Erica E M Moodie
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada.
| | - Janie Coulombe
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada
| | - Coraline Danieli
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada
| | - Christel Renoux
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada
| | - Susan M Shortreed
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, USA
- Biostatistics Department, University of Washington, Seattle, USA
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