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The handling of missing data in trial-based economic evaluations: should data be multiply imputed prior to longitudinal linear mixed-model analyses?
IntroductionFor the analysis of clinical effects, multiple imputation (MI) of missing data were shown to be unnecessary when using longitudinal...
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Impute the missing data using retrieved dropouts
BackgroundIn the past few decades various methods have been proposed to handle missing data of clinical studies, so as to assess the robustness of...
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Quantifying bias due to missing data in quality of life surveys of advanced-stage cancer patients
PurposeMany studies on cancer patients investigate the impact of treatment on health-related quality of life (QoL). Typically, QoL is measured...
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Methodological issues of the electronic health records’ use in the context of epidemiological investigations, in light of missing data: a review of the recent literature
BackgroundElectronic health records (EHRs) are widely accepted to enhance the health care quality, patient monitoring, and early prevention of...
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Using observational study data as an external control group for a clinical trial: an empirical comparison of methods to account for longitudinal missing data
BackgroundObservational data are increasingly being used to conduct external comparisons to clinical trials. In this study, we empirically examined...
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Learning from missing data: examining nonreporting patterns of height, weight, and BMI among Canadian youth
BackgroundYouth body mass index (BMI), derived from self-reported height and weight, is commonly prone to nonreporting. A considerable proportion of...
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Missing data imputation, prediction, and feature selection in diagnosis of vaginal prolapse
BackgroundData loss often occurs in the collection of clinical data. Directly discarding the incomplete sample may lead to low accuracy of medical...
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Non-invasive Hemoglobin Measurement Predictive Analytics with Missing Data and Accuracy Improvement Using Gaussian Process and Functional Regression Model
Recent use of noninvasive and continuous hemoglobin (SpHb) concentration monitor has emerged as an alternative to invasive laboratory-based...
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Dealing with missing data in laboratory test results used as a baseline covariate: results of multi-hospital cohort studies utilizing a database system contributing to MID-NET® in Japan
BackgroundTo evaluate missing data methods applied to laboratory test results used for confounding adjustment, utilizing data from 10 MID-NET ® -collab...
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Imputation of missing values for electronic health record laboratory data
Laboratory data from Electronic Health Records (EHR) are often used in prediction models where estimation bias and model performance from missingness...
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The effect of high prevalence of missing data on estimation of the coefficients of a logistic regression model when using multiple imputation
BackgroundMultiple imputation is frequently used to address missing data when conducting statistical analyses. There is a paucity of research into...
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The reporting and handling of missing data in longitudinal studies of older adults is suboptimal: a methodological survey of geriatric journals
BackgroundMissing data are common in longitudinal studies, and more so, in studies of older adults, who are susceptible to health and functional...
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The effect of missing data and imputation on the detection of bias in cognitive testing using differential item functioning methods
BackgroundItem response theory (IRT) methods for addressing differential item functioning (DIF) can detect group differences in responses to...
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What difference does multiple imputation make in longitudinal modeling of EQ-5D-5L data? Empirical analyses of simulated and observed missing data patterns
PurposeAlthough multiple imputation is the state-of-the-art method for managing missing data, mixed models without multiple imputation may be equally...
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Generative adversarial networks for imputing missing data for big data clinical research
BackgroundMissing data is a pervasive problem in clinical research. Generative adversarial imputation nets (GAIN), a novel machine learning data...
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Multiple imputation methods for missing multilevel ordinal outcomes
BackgroundMultiple imputation (MI) is an established technique for handling missing data in observational studies. Joint modelling (JM) and fully...
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The impact of imputation quality on machine learning classifiers for datasets with missing values
BackgroundClassifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in...
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A flexible approach for variable selection in large-scale healthcare database studies with missing covariate and outcome data
BackgroundPrior work has shown that combining bootstrap imputation with tree-based machine learning variable selection methods can provide good...
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Logical definition-based identification of potential missing concepts in SNOMED CT
BackgroundBiomedical ontologies are representations of biomedical knowledge that provide terms with precisely defined meanings. They play a vital...
