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BrainGENIE: The Brain Gene Expression and Network Imputation Engine
In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. To address this problem, we developed a...
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Inference following multiple imputation for generalized additive models: an investigation of the median p-value rule with applications to the Pulmonary Hypertension Association Registry and Colorado COVID-19 hospitalization data
BackgroundMissing data prove troublesome in data analysis; at best they reduce a study’s statistical power and at worst they induce bias in parameter...
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The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes
Aims/hypothesisThe Latino population has been systematically underrepresented in large-scale genetic analyses, and previous studies have relied on...
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Evaluation of data imputation strategies in complex, deeply-phenotyped data sets: the case of the EU-AIMS Longitudinal European Autism Project
An increasing number of large-scale multi-modal research initiatives has been conducted in the typically develo** population, e.g. Dev. Cogn. Neur....
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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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A review of the use of controlled multiple imputation in randomised controlled trials with missing outcome data
BackgroundMissing data are common in randomised controlled trials (RCTs) and can bias results if not handled appropriately. A statistically valid...
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Multiple imputation validation study: addressing unmeasured survey data in a longitudinal design
BackgroundQuestionnaires used in longitudinal studies may have questions added or removed over time for numerous reasons. Data missing completely at...
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Standard multiple imputation of survey data didn’t perform better than simple substitution in enhancing an administrative dataset: the example of self-rated health in England
BackgroundHealth surveys provide a rich array of information but on relatively small numbers of individuals and evidence suggests that they are...
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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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Addressing missing values in routine health information system data: an evaluation of imputation methods using data from the Democratic Republic of the Congo during the COVID-19 pandemic
BackgroundPoor data quality is limiting the use of data sourced from routine health information systems (RHIS), especially in low- and middle-income...
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Multiple imputation with missing indicators as proxies for unmeasured variables: simulation study
BackgroundWithin routinely collected health data, missing data for an individual might provide useful information in itself. This occurs, for...
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Missing not at random in end of life care studies: multiple imputation and sensitivity analysis on data from the ACTION study
BackgroundMissing data are common in end-of-life care studies, but there is still relatively little exploration of which is the best method to deal...
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Multiple imputation by predictive mean matching in cluster-randomized trials
BackgroundRandom effects regression imputation has been recommended for multiple imputation (MI) in cluster randomized trials (CRTs) because it is...
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Application of machine learning missing data imputation techniques in clinical decision making: taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example
BackgroundThere are often many missing values in medical data, which directly affect the accuracy of clinical decision making. Discharge assessment...
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Evaluation of approaches for multiple imputation of three-level data
BackgroundThree-level data arising from repeated measures on individuals who are clustered within larger units are common in health research studies....
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A human leukocyte antigen imputation study uncovers possible genetic interplay between gut inflammatory processes and autism spectrum disorders
Autism spectrum disorders (ASD) are neurodevelopmental conditions that are for subsets of individuals, underpinned by dysregulated immune processes,...
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Adjusting for verification bias in diagnostic accuracy measures when comparing multiple screening tests - an application to the IP1-PROSTAGRAM study
IntroductionNovel screening tests used to detect a target condition are compared against either a reference standard or other existing screening...
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Multiple imputation to quantify misclassification in observational studies of the cognitively impaired: an application for pain assessment in nursing home residents
BackgroundDespite experimental evidence suggesting that pain sensitivity is not impaired by cognitive impairment, observational studies in nursing...
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Examining Associations Between Multiple Types of IPV and Adverse Mental Health Among IPV Survivors
PurposeResearch shows that women who experience intimate partner violence (IPV) are at risk for a range of adverse mental health outcomes, including...
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Methods to improve the quality of smoking records in a primary care EMR database: exploring multiple imputation and pattern-matching algorithms
BackgroundPrimary care electronic medical record (EMR) data are emerging as a useful source for secondary uses, such as disease surveillance, health...
