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What is the difference between missing completely at random and missing at random? [PDF]
International Journal of Epidemiology, 2014 The terminology describing missingness mechanisms is confusing. In particular the meaning of 'missing at random' is often misunderstood, leading researchers faced with missing data problems away from multiple imputation, a method with considerable ...
Krishnan Bhaskaran, Liam Smeeth
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Little's Test of Missing Completely at Random [PDF]
The Stata Journal, 2013 In missing-data analysis, Little’s test (1988, Journal of the American Statistical Association 83: 1198–1202) is useful for testing the assumption of missing completely at random for multivariate, partially observed quantitative data. I introduce the mcartest command, which implements Little’s missing completely at random test and its extension for ...
Li, Cheng, Li, Cheng
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Optimal nonparametric testing of Missing Completely At Random and its connections to compatibility
Annals of Statistics, 2023 Given a set of incomplete observations, we study the nonparametric problem of testing whether data are Missing Completely At Random (MCAR). Our first contribution is to characterise precisely the set of alternatives that can be distinguished from the MCAR null hypothesis.
Berrett, Thomas B, Samworth, Richard J
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Outcome-sensitive multiple imputation: a simulation study [PDF]
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
doaj +9 more sources
Missing data and multiple imputation in clinical epidemiological research [PDF]
Clinical Epidemiology, 2017 Alma B Pedersen,1 Ellen M Mikkelsen,1 Deirdre Cronin-Fenton,1 Nickolaj R Kristensen,1 Tra My Pham,2 Lars Pedersen,1 Irene Petersen1,2 1Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus N, Denmark; 2Department of Primary Care and ...
Pedersen AB +6 more
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Tests of missing completely at random based on sample covariance matrices
Annals of StatisticsWe study the problem of testing whether the missing values of a potentially high-dimensional dataset are Missing Completely at Random (MCAR). We relax the problem of testing MCAR to the problem of testing the compatibility of a collection of covariance matrices, motivated by the fact that this procedure is feasible when the dimension grows with the ...
Bordino, Alberto, Berrett, Thomas B.
exaly +3 more sources
Impact on Cronbach's alpha of simple treatment methods for missing data [PDF]
Tutorials in Quantitative Methods for Psychology, 2016 The scientific treatment of missing data has been the subject of research for nearly a century. Strangely, interest in missing data is quite new in the fields of educational science and psychology (Peugh & Enders, 2004; Schafer & Graham, 2002). It is now
Béland, Sébastien +2 more
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Regression-Based Approach to Test Missing Data Mechanisms
Data, 2022 Missing data occur in almost all surveys; in order to handle them correctly it is essential to know their type. Missing data are generally divided into three types (or generating mechanisms): missing completely at random, missing at random, and missing ...
Serguei Rouzinov, André Berchtold
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Multi-Domain Image Completion for Random Missing Input Data [PDF]
IEEE Transactions on Medical Imaging, 2021 Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e.g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI). However, due to possible data corruption and different imaging protocols, the availability of images for each domain could vary ...
Liyue Shen +11 more
openaire +3 more sources
BMC Medical Research Methodology, 2023 Background Detecting treatment effect heterogeneity is an important objective in cluster randomized trials and implementation research. While sample size procedures for testing the average treatment effect accounting for participant attrition assuming ...
Jiaqi Tong +3 more
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