Handling of missing data with multiple imputation in observational studies that address causal questions: protocol for a scoping review
Introduction Observational studies in health-related research often aim to answer causal questions. Missing data are common in these studies and often occur in multiple variables, such as the exposure, outcome and/or variables used to control for confounding. The standard classification of missing d...
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BMJ Publishing Group
2023-02-01
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| author | John Carlin Julie Simpson Katherine Lee Cattram Nguyen Rheanna Mainzer Margarita Moreno-Betancur |
| author_facet | John Carlin Julie Simpson Katherine Lee Cattram Nguyen Rheanna Mainzer Margarita Moreno-Betancur |
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| description | Introduction Observational studies in health-related research often aim to answer causal questions. Missing data are common in these studies and often occur in multiple variables, such as the exposure, outcome and/or variables used to control for confounding. The standard classification of missing data as missing completely at random, missing at random (MAR) or missing not at random does not allow for a clear assessment of missingness assumptions when missingness arises in more than one variable. This presents challenges for selecting an analytic approach and determining when a sensitivity analysis under plausible alternative missing data assumptions is required. This is particularly pertinent with multiple imputation (MI), which is often justified by assuming data are MAR. The objective of this scoping review is to examine the use of MI in observational studies that address causal questions, with a focus on if and how (a) missingness assumptions are expressed and assessed, (b) missingness assumptions are used to justify the choice of a complete case analysis and/or MI for handling missing data and (c) sensitivity analyses under alternative plausible assumptions about the missingness mechanism are conducted.Methods and analysis We will review observational studies that aim to answer causal questions and use MI, published between January 2019 and December 2021 in five top general epidemiology journals. Studies will be identified using a full text search for the term ‘multiple imputation’ and then assessed for eligibility. Information extracted will include details about the study characteristics, missing data, missingness assumptions and MI implementation. Data will be summarised using descriptive statistics.Ethics and dissemination Ethics approval is not required for this review because data will be collected only from published studies. The results will be disseminated through a peer reviewed publication and conference presentations.Trial registration number This protocol is registered on figshare (https://doi.org/10.6084/m9.figshare.20010497.v1). |
| format | Article |
| id | doaj-art-6f9679214bc042d893a5ecaecb953039 |
| institution | DOAJ |
| issn | 2044-6055 |
| language | English |
| publishDate | 2023-02-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-6f9679214bc042d893a5ecaecb9530392025-08-20T03:02:04ZengBMJ Publishing GroupBMJ Open2044-60552023-02-0113210.1136/bmjopen-2022-065576Handling of missing data with multiple imputation in observational studies that address causal questions: protocol for a scoping reviewJohn Carlin0Julie Simpson1Katherine Lee2Cattram Nguyen3Rheanna Mainzer4Margarita Moreno-Betancur5School of Population and Global Health, University of Melbourne, Parkville, Victoria, AustraliaCentre for Epidemiology and Biostatistics, University of Melbourne School of Population and Global Health, Melbourne, Victoria, AustraliaClinical Epidemiology & Biostatistics Unit, Murdoch Children`s Research Institute, Melbourne, Victoria, AustraliaClinical Epidemiology and Biostatistics Unit, Murdoch Children’s Research Institute, Parkville, Victoria, Australia4 Department of Paediatrics, The University of Melbourne, Parkville, Victoria, AustraliaClinical Epidemiology & Biostatistics Unit, Murdoch Children’s Research Institute, Melbourne, Victoria, AustraliaIntroduction Observational studies in health-related research often aim to answer causal questions. Missing data are common in these studies and often occur in multiple variables, such as the exposure, outcome and/or variables used to control for confounding. The standard classification of missing data as missing completely at random, missing at random (MAR) or missing not at random does not allow for a clear assessment of missingness assumptions when missingness arises in more than one variable. This presents challenges for selecting an analytic approach and determining when a sensitivity analysis under plausible alternative missing data assumptions is required. This is particularly pertinent with multiple imputation (MI), which is often justified by assuming data are MAR. The objective of this scoping review is to examine the use of MI in observational studies that address causal questions, with a focus on if and how (a) missingness assumptions are expressed and assessed, (b) missingness assumptions are used to justify the choice of a complete case analysis and/or MI for handling missing data and (c) sensitivity analyses under alternative plausible assumptions about the missingness mechanism are conducted.Methods and analysis We will review observational studies that aim to answer causal questions and use MI, published between January 2019 and December 2021 in five top general epidemiology journals. Studies will be identified using a full text search for the term ‘multiple imputation’ and then assessed for eligibility. Information extracted will include details about the study characteristics, missing data, missingness assumptions and MI implementation. Data will be summarised using descriptive statistics.Ethics and dissemination Ethics approval is not required for this review because data will be collected only from published studies. The results will be disseminated through a peer reviewed publication and conference presentations.Trial registration number This protocol is registered on figshare (https://doi.org/10.6084/m9.figshare.20010497.v1).https://bmjopen.bmj.com/content/13/2/e065576.full |
| spellingShingle | John Carlin Julie Simpson Katherine Lee Cattram Nguyen Rheanna Mainzer Margarita Moreno-Betancur Handling of missing data with multiple imputation in observational studies that address causal questions: protocol for a scoping review BMJ Open |
| title | Handling of missing data with multiple imputation in observational studies that address causal questions: protocol for a scoping review |
| title_full | Handling of missing data with multiple imputation in observational studies that address causal questions: protocol for a scoping review |
| title_fullStr | Handling of missing data with multiple imputation in observational studies that address causal questions: protocol for a scoping review |
| title_full_unstemmed | Handling of missing data with multiple imputation in observational studies that address causal questions: protocol for a scoping review |
| title_short | Handling of missing data with multiple imputation in observational studies that address causal questions: protocol for a scoping review |
| title_sort | handling of missing data with multiple imputation in observational studies that address causal questions protocol for a scoping review |
| url | https://bmjopen.bmj.com/content/13/2/e065576.full |
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