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Inquiry: The Journal of Health Care Organization, Provision, and Financing, 2022 Secondary datasets are used in healthcare research because of its cost advantages, its convenience, and the size of the datasets. However, missing data can cause problems that are difficult to resolve.
Soojung Jo PhD, RN
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PLoS ONE, 2020 BackgroundCommercial physical activity monitors have wide utility in the assessment of physical activity in research and clinical settings, however, the removal of devices results in missing data and has the potential to bias study conclusions.
R O'Driscoll +8 more
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A note on multiple imputation for method of moments estimation [PDF]
, 2015 Multiple imputation is a popular imputation method for general purpose estimation. Rubin(1987) provided an easily applicable formula for the variance estimation of multiple imputation.
Kim, Jae Kwang +2 more
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A comparison of imputation methods for categorical data
Informatics in Medicine Unlocked, 2023 Objectives: Missing data is commonplace in clinical databases, which are being increasingly used for research. Without giving any regard to missing data, results from analysis may become biased and unrepresentative.
Shaheen MZ. Memon +2 more
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The R Package hmi: A Convenient Tool for Hierarchical Multiple Imputation and Beyond
Journal of Statistical Software, 2020 Applications of multiple imputation have long outgrown the traditional context of dealing with item nonresponse in cross-sectional data sets. Nowadays multiple imputation is also applied to impute missing values in hierarchical data sets, address ...
Matthias Speidel +2 more
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JAMA, 2015 In this issue of JAMA, Asch et al1 report results of a cluster-randomized clinical trial designed to evaluate the effects of physician financial incentives, patient incentives, or shared physician and patient incentives on low density lipoprotein cholesterol (LDL-C) levels among patients with high cardiovascular risk.
Peng, Li +2 more
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Effects of Different Missing Data Imputation Techniques on the Performance of Undiagnosed Diabetes Risk Prediction Models in a Mixed-Ancestry Population of South Africa. [PDF]
PLoS ONE, 2015 Imputation techniques used to handle missing data are based on the principle of replacement. It is widely advocated that multiple imputation is superior to other imputation methods, however studies have suggested that simple methods for filling missing ...
Katya L Masconi +3 more
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Recovery of information from multiple imputation: a simulation study
Emerging Themes in Epidemiology, 2012 Background Multiple imputation is becoming increasingly popular for handling missing data. However, it is often implemented without adequate consideration of whether it offers any advantage over complete case analysis for the research question of ...
Lee Katherine J, Carlin John B
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Model checking in multiple imputation: an overview and case study
Emerging Themes in Epidemiology, 2017 Background Multiple imputation has become very popular as a general-purpose method for handling missing data. The validity of multiple-imputation-based analyses relies on the use of an appropriate model to impute the missing values.
Cattram D. Nguyen +2 more
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SAGE Open Medicine, 2019 Objectives: Missing data is a recurrent issue in many fields of medical research, particularly in questionnaires. The aim of this article is to describe and compare six conceptually different multiple imputation methods, alongside the commonly used ...
Marianne Riksheim Stavseth +2 more
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