1
|
Mainzer RM, Moreno-Betancur M, Nguyen CD, Simpson JA, Carlin JB, Lee KJ. Gaps in the usage and reporting of multiple imputation for incomplete data: findings from a scoping review of observational studies addressing causal questions. BMC Med Res Methodol 2024; 24:193. [PMID: 39232661 PMCID: PMC11373423 DOI: 10.1186/s12874-024-02302-6] [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: 05/21/2024] [Accepted: 08/02/2024] [Indexed: 09/06/2024] Open
Abstract
BACKGROUND Missing data are common in observational studies and often occur in several of the variables required when estimating a causal effect, i.e. the exposure, outcome and/or variables used to control for confounding. Analyses involving multiple incomplete variables are not as straightforward as analyses with a single incomplete variable. For example, in the context of multivariable missingness, the standard missing data assumptions ("missing completely at random", "missing at random" [MAR], "missing not at random") are difficult to interpret and assess. It is not clear how the complexities that arise due to multivariable missingness are being addressed in practice. The aim of this study was to review how missing data are managed and reported in observational studies that use multiple imputation (MI) for causal effect estimation, with a particular focus on missing data summaries, missing data assumptions, primary and sensitivity analyses, and MI implementation. METHODS We searched five top general epidemiology journals for observational studies that aimed to answer a causal research question and used MI, published between January 2019 and December 2021. Article screening and data extraction were performed systematically. RESULTS Of the 130 studies included in this review, 108 (83%) derived an analysis sample by excluding individuals with missing data in specific variables (e.g., outcome) and 114 (88%) had multivariable missingness within the analysis sample. Forty-four (34%) studies provided a statement about missing data assumptions, 35 of which stated the MAR assumption, but only 11/44 (25%) studies provided a justification for these assumptions. The number of imputations, MI method and MI software were generally well-reported (71%, 75% and 88% of studies, respectively), while aspects of the imputation model specification were not clear for more than half of the studies. A secondary analysis that used a different approach to handle the missing data was conducted in 69/130 (53%) studies. Of these 69 studies, 68 (99%) lacked a clear justification for the secondary analysis. CONCLUSION Effort is needed to clarify the rationale for and improve the reporting of MI for estimation of causal effects from observational data. We encourage greater transparency in making and reporting analytical decisions related to missing data.
Collapse
Affiliation(s)
- Rheanna M Mainzer
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Parkville, Victoria, 3052, Australia.
- Department of Paediatrics, The University of Melbourne, Parkville, Victoria, 3052, Australia.
| | - Margarita Moreno-Betancur
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Parkville, Victoria, 3052, Australia
- Department of Paediatrics, The University of Melbourne, Parkville, Victoria, 3052, Australia
| | - Cattram D Nguyen
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Parkville, Victoria, 3052, Australia
- Department of Paediatrics, The University of Melbourne, Parkville, Victoria, 3052, Australia
| | - Julie A Simpson
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, 3052, Australia
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - John B Carlin
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Parkville, Victoria, 3052, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, 3052, Australia
| | - Katherine J Lee
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Parkville, Victoria, 3052, Australia
- Department of Paediatrics, The University of Melbourne, Parkville, Victoria, 3052, Australia
| |
Collapse
|
2
|
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 2024; 25:633-647. [PMID: 37660312 DOI: 10.1093/biostatistics/kxad022] [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: 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.
Collapse
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
| |
Collapse
|
3
|
Wang S, Zhang J, Zhu T, Xie X, Xia X, Li Y. Efficacy of Magnesium Sulfate and Labetalol in the Treatment of Pregnancy-Induced Hypertension and Its Effect on Anxiety and Depression: A Retrospective Cohort Study. ALPHA PSYCHIATRY 2024; 25:243-248. [PMID: 38798818 PMCID: PMC11117433 DOI: 10.5152/alphapsychiatry.2024.231342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 02/26/2024] [Indexed: 05/29/2024]
Abstract
Background In this study, the effect of magnesium sulfate and labetalol in treating pregnancy-induced hypertension (PIH) and its influence on anxiety and depression in patients are observed, and new ideas for treating anxiety and depression in PIH are introduced. Methods A retrospective cohort study was conducted to select patients with PlH diagnosed from July 2020 to July 2023 from Affiliated Hospital of Electronic Science and Technology University and Chengdu Women' s and Children's Central Hospital in Chengdu of Sichuan Province. The changes in blood pressure, Edinburgh Postnatal Depression Scale (EPDS), and generalized anxiety disorder 7 (GAD-7) in patients with hypertensive pregnancy were collected and analyzed. Results In our investigation, 219 patients completed the study, and 36.1% (79/219) of them developed anxiety and depression. According to whether the patients were treated with magnesium sulfate and labetalol, 49 cases were assigned to the magnesium sulfate and labetalol treatment (MSLT) group, and 30 cases were assigned to the conventional treatment (CT) group. Edinburgh Postnatal Depression Scale scores and GAD-7 scores in the MSLT group were significantly lower than those in the CT group, indicating that magnesium sulfate and labetalol can improve anxiety and depression in hypertensive patients during pregnancy. The difference was statistically significant (P < .05). According to the changes in systolic blood pressure, the clinical efficacy of patients was evaluated, and no significant difference in efficacy existed between the MSLT and CT groups. Conclusion Magnesium sulfate and labetalol can control the blood pressure of patients with PIH and indirectly improve anxiety and depression in patients with PIH, thereby introducing new ideas for the treatment of PIH accompanied by anxiety and depression.
Collapse
Affiliation(s)
- Siyi Wang
- Department of Obstetrics, Affiliated Hospital of Electronic Science and Technology University, UESTC, Chengdu Women’s and Children’s Central Hospital, Sichuan, China
| | - Jiajia Zhang
- Department of Obstetrics, Affiliated Hospital of Electronic Science and Technology University, UESTC, Chengdu Women’s and Children’s Central Hospital, Sichuan, China
| | - Tianying Zhu
- Department of Obstetrics, Affiliated Hospital of Electronic Science and Technology University, UESTC, Chengdu Women’s and Children’s Central Hospital, Sichuan, China
| | - Xiaoxiao Xie
- Department of Obstetrics, Affiliated Hospital of Electronic Science and Technology University, UESTC, Chengdu Women’s and Children’s Central Hospital, Sichuan, China
| | - Xin Xia
- Department of Obstetrics, Affiliated Hospital of Electronic Science and Technology University, UESTC, Chengdu Women’s and Children’s Central Hospital, Sichuan, China
| | - Yan Li
- Department of Obstetrics, Affiliated Hospital of Electronic Science and Technology University, UESTC, Chengdu Women’s and Children’s Central Hospital, Sichuan, China
| |
Collapse
|
4
|
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: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [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.
Collapse
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
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
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] [Abstract] [Key Words] [MESH Headings] [Grants] [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.
Collapse
Affiliation(s)
- Hong Cai
- 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
| | - Wei Bai
- 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
| | - Xiangdong Du
- Guangji Hospital Affiliated to Soochow University, Suzhou, Jiangsu province, China
| | - Ling Zhang
- Nanning Fifth People's Hospital, Nanning, Guangxi province, China
| | - Lan Zhang
- Department of Psychiatry, Lanzhou University Second Hospital, Lanzhou, Gansu province, China
| | - Yu-Chen Li
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Huan-Zhong Liu
- Department of Psychiatry, Chaohu Hospital, Anhui Medical University, Hefei, China
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
| | - Yi-Lang Tang
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
- Atlanta VA Medical Center, Atlanta, GA, USA
| | - Todd Jackson
- Department of Psychology, University of Macau, Macao, Macao SAR, China
| | - Teris Cheung
- School 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.
| |
Collapse
|
7
|
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] [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
| |
Collapse
|
8
|
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 ANALYSIS 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] [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.
Collapse
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
| |
Collapse
|
9
|
Coulombe J, Moodie EEM, Shortreed SM, Renoux C. Coulombe et al. Respond to "Baby Steps to a Learning Mental Health-Care System". Am J Epidemiol 2021; 190:1223-1224. [PMID: 33295984 DOI: 10.1093/aje/kwaa262] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 11/23/2020] [Accepted: 12/04/2020] [Indexed: 12/19/2022] Open
|