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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.
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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
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Lund JL, Webster-Clark MA, Westreich D, Sanoff HK, Robert N, Frytak JR, Boyd M, Shmuel S, Stürmer T, Keil AP. Visualizing External Validity: Graphical Displays to Inform the Extension of Treatment Effects from Trials to Clinical Practice. Epidemiology 2024; 35:241-251. [PMID: 38290143 PMCID: PMC10826920 DOI: 10.1097/ede.0000000000001694] [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: 04/06/2023] [Accepted: 11/13/2023] [Indexed: 02/01/2024]
Abstract
BACKGROUND In the presence of effect measure modification, estimates of treatment effects from randomized controlled trials may not be valid in clinical practice settings. The development and application of quantitative approaches for extending treatment effects from trials to clinical practice settings is an active area of research. METHODS In this article, we provide researchers with a practical roadmap and four visualizations to assist in variable selection for models to extend treatment effects observed in trials to clinical practice settings and to assess model specification and performance. We apply this roadmap and visualizations to an example extending the effects of adjuvant chemotherapy (5-fluorouracil vs. plus oxaliplatin) for colon cancer from a trial population to a population of individuals treated in community oncology practices in the United States. RESULTS The first visualization screens for potential effect measure modifiers to include in models extending trial treatment effects to clinical practice populations. The second visualization displays a measure of covariate overlap between the clinical practice populations and the trial population. The third and fourth visualizations highlight considerations for model specification and influential observations. The conceptual roadmap describes how the output from the visualizations helps interrogate the assumptions required to extend treatment effects from trials to target populations. CONCLUSIONS The roadmap and visualizations can inform practical decisions required for quantitatively extending treatment effects from trials to clinical practice settings.
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Affiliation(s)
- Jennifer L. Lund
- From the Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
- University of North Carolina Lineberger Comprehensive Cancer Center, Chapel Hill, NC
| | - Michael A. Webster-Clark
- From the Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Department of Epidemiology and Biostatistics, McGill University, Montreal, Canada
| | - Daniel Westreich
- From the Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Hanna K. Sanoff
- University of North Carolina Lineberger Comprehensive Cancer Center, Chapel Hill, NC
| | | | | | | | - Shahar Shmuel
- From the Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Til Stürmer
- From the Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
- University of North Carolina Lineberger Comprehensive Cancer Center, Chapel Hill, NC
| | - Alexander P. Keil
- From the Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
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Swilley-Martinez ME, Coles SA, Miller VE, Alam IZ, Fitch KV, Cruz TH, Hohl B, Murray R, Ranapurwala SI. "We adjusted for race": now what? A systematic review of utilization and reporting of race in American Journal of Epidemiology and Epidemiology, 2020-2021. Epidemiol Rev 2023; 45:15-31. [PMID: 37789703 DOI: 10.1093/epirev/mxad010] [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: 05/16/2022] [Revised: 07/31/2023] [Accepted: 09/28/2023] [Indexed: 10/05/2023] Open
Abstract
Race is a social construct, commonly used in epidemiologic research to adjust for confounding. However, adjustment of race may mask racial disparities, thereby perpetuating structural racism. We conducted a systematic review of articles published in Epidemiology and American Journal of Epidemiology between 2020 and 2021 to (1) understand how race, ethnicity, and similar social constructs were operationalized, used, and reported; and (2) characterize good and poor practices of utilization and reporting of race data on the basis of the extent to which they reveal or mask systemic racism. Original research articles were considered for full review and data extraction if race data were used in the study analysis. We extracted how race was categorized, used-as a descriptor, confounder, or for effect measure modification (EMM)-and reported if the authors discussed racial disparities and systemic bias-related mechanisms responsible for perpetuating the disparities. Of the 561 articles, 299 had race data available and 192 (34.2%) used race data in analyses. Among the 160 US-based studies, 81 different racial categorizations were used. Race was most often used as a confounder (52%), followed by effect measure modifier (33%), and descriptive variable (12%). Fewer than 1 in 4 articles (22.9%) exhibited good practices (EMM along with discussing disparities and mechanisms), 63.5% of the articles exhibited poor practices (confounding only or not discussing mechanisms), and 13.5% were considered neither poor nor good practices. We discuss implications and provide 13 recommendations for operationalization, utilization, and reporting of race in epidemiologic and public health research.
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Affiliation(s)
- Monica E Swilley-Martinez
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599-7435, United States
- Injury Prevention Research Center, University of North Carolina, Chapel Hill, NC 27599, United States
| | - Serita A Coles
- Department of Health Behavior, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599-7440, United States
| | - Vanessa E Miller
- Injury Prevention Research Center, University of North Carolina, Chapel Hill, NC 27599, United States
| | - Ishrat Z Alam
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599-7435, United States
- Injury Prevention Research Center, University of North Carolina, Chapel Hill, NC 27599, United States
| | - Kate Vinita Fitch
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599-7435, United States
- Injury Prevention Research Center, University of North Carolina, Chapel Hill, NC 27599, United States
| | - Theresa H Cruz
- Prevention Research Center, Department of Pediatrics, Health Sciences Center, University of New Mexico, Albuquerque, NM 87131, United States
| | - Bernadette Hohl
- Penn Injury Science Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104-6021, United States
| | - Regan Murray
- Center for Public Health and Technology, Department of Health, Human Performance and Recreation, University of Arkansas, Fayetteville, AR 72701, United States
| | - Shabbar I Ranapurwala
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599-7435, United States
- Injury Prevention Research Center, University of North Carolina, Chapel Hill, NC 27599, United States
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Rudolph KE, Díaz I. Efficiently transporting causal direct and indirect effects to new populations under intermediate confounding and with multiple mediators. Biostatistics 2022; 23:789-806. [PMID: 33528006 PMCID: PMC9295139 DOI: 10.1093/biostatistics/kxaa057] [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: 07/10/2020] [Revised: 11/16/2020] [Accepted: 11/28/2020] [Indexed: 11/12/2022] Open
Abstract
The same intervention can produce different effects in different sites. Existing transport mediation estimators can estimate the extent to which such differences can be explained by differences in compositional factors and the mechanisms by which mediating or intermediate variables are produced; however, they are limited to consider a single, binary mediator. We propose novel nonparametric estimators of transported interventional (in)direct effects that consider multiple, high-dimensional mediators and a single, binary intermediate variable. They are multiply robust, efficient, asymptotically normal, and can incorporate data-adaptive estimation of nuisance parameters. They can be applied to understand differences in treatment effects across sites and/or to predict treatment effects in a target site based on outcome data in source sites.
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Affiliation(s)
- Kara E Rudolph
- Department of Epidemiology, Mailman School of Public Health, Columbia
University; and Division of Biostatistics, Department of Population
Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Iván Díaz
- Department of Epidemiology, Mailman School of Public Health, Columbia
University; and Division of Biostatistics, Department of Population
Health Sciences, Weill Cornell Medicine, New York, NY, USA
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