Results 21 to 30 of about 549,387 (258)
BMC Medical Research Methodology, 2017 Background Within epidemiological and clinical research, missing data are a common issue and often over looked in publications. When the issue of missing observations is addressed it is usually assumed that the missing data are ‘missing at random’ (MAR).
M. Smuk, J. R. Carpenter, T. P. Morris
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Impute the missing data using retrieved dropouts
BMC Medical Research Methodology, 2022 Background In the past few decades various methods have been proposed to handle missing data of clinical studies, so as to assess the robustness of primary results.
Shuai Wang, Haoyan Hu
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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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Imputation and low-rank estimation with Missing Not At Random data [PDF]
Statistics and Computing, 2020 Missing values challenge data analysis because many supervised and unsupervised learning methods cannot be applied directly to incomplete data. Matrix completion based on low-rank assumptions are very powerful solution for dealing with missing values.
Sportisse, Aude +2 more
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Score test for missing at random or not
, 2021 Missing data are frequently encountered in various disciplines and can be divided into three categories: missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). Valid statistical approaches to missing data depend crucially on correct identification of the underlying missingness mechanism.
Wang, Hairu, Lu, Zhiping, Liu, Yukun
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A Robust Classifier under Missing-Not-at-Random Sample Selection Bias
2023 IEEE International Conference on Big Data (BigData), 2023 The shift between the training and testing distributions is commonly due to sample selection bias, a type of bias caused by non-random sampling of examples to be included in the training set. Although there are many approaches proposed to learn a classifier under sample selection bias, few address the case where a subset of labels in the training set ...
Mai, Huy +3 more
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BMC Medical Research Methodology, 2019 Background The benefit of a given treatment can be evaluated via a randomized clinical trial design. However, protocol deviations may severely compromise treatment effect since such deviations often lead to missing values.
Abdul-Karim Iddrisu, Freedom Gumedze
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Comparison of Different LGM-Based Methods with MAR and MNAR Dropout Data
Frontiers in Psychology, 2017 The missing not at random (MNAR) mechanism may bias parameter estimates and even distort study results. This study compared the maximum likelihood (ML) selection model based on missing at random (MAR) mechanism and the Diggle–Kenward selection model ...
Meijuan Li +5 more
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Consequences of multiple imputation of missing standard deviations and sample sizes in meta‐analysis
Ecology and Evolution, 2020 Meta‐analyses often encounter studies with incompletely reported variance measures (e.g., standard deviation values) or sample sizes, both needed to conduct weighted meta‐analyses.
Stephan Kambach +5 more
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Journal of Clinical and Translational Science, 2021 Objectives/Goals: The association between surgery with general anesthesia (exposure) and cognition (outcome) among older adults has been studied with mixed conclusions.
Katrina L. Devick +4 more
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