Results 11 to 20 of about 159,661 (256)
A Noise-Aware Multiple Imputation Algorithm for Missing Data
Mathematics, 2022 Missing data is a common and inevitable phenomenon. In practical applications, the datasets usually contain noises for various reasons. Most of the existing missing data imputing algorithms are affected by noises which reduce the accuracy of the ...
Fangfang Li, Hui Sun, Yu Gu, Ge Yu
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Statistical inference for Hardy-Weinberg proportions in the presence of missing genotype information. [PDF]
PLoS ONE, 2013 In genetic association studies, tests for Hardy-Weinberg proportions are often employed as a quality control checking procedure. Missing genotypes are typically discarded prior to testing.
Jan Graffelman +3 more
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BMC Medical Research Methodology, 2022 Background Multiple imputation is frequently used to address missing data when conducting statistical analyses. There is a paucity of research into the performance of multiple imputation when the prevalence of missing data is very high. Our objective was
Peter C. Austin, Stef van Buuren
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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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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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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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Outcome-sensitive multiple imputation: a simulation study
BMC Medical Research Methodology, 2017 Background Multiple imputation is frequently used to deal with missing data in healthcare research. Although it is known that the outcome should be included in the imputation model when imputing missing covariate values, it is not known whether it should
Evangelos Kontopantelis +3 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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