Results 11 to 20 of about 199,171 (299)
JMIR Public Health and SurveillanceBackgroundImmunization Information Systems (IIS) and surveillance data are essential for public health interventions and programming; however, missing data are often a challenge, potentially introducing bias and impacting the accuracy of vaccine coverage
Sara Brown +5 more
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Evaluation of Multi-parameter Test Statistics for Multiple Imputation
Multivariate Behavioral Research, 2017 In Ordinary Least Square regression, researchers often are interested in knowing whether a set of parameters is different from zero. With complete data, this could be achieved using the gain in prediction test, hierarchical multiple regression, or an omnibus F test. However, in substantive research scenarios, missing data often exist. In the context of
Yu Liu, Craig K. Enders
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Accurate and adaptive imputation of summary statistics in mixed-ethnicity cohorts [PDF]
Bioinformatics, 2018 Abstract Motivation Methods based on summary statistics obtained from genome-wide association studies have gained considerable interest in genetics due to the computational cost and privacy advantages they present.
Matteo Togninalli +3 more
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xQTLImp: efficient and accurate xQTL summary statistics imputation [PDF]
, 2019 AbstractMotivationQuantitative trait locus (QTL) analysis of multiomic molecular traits, such as gene transcription (eQTL), DNA methylation (mQTL) and histone modification (haQTL), has been widely used to infer the effects of genomic variation on multiple levels of molecular activities.
Tao Wang +5 more
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Advanced Statistics: Missing Data in Clinical Research—Part 2: Multiple Imputation [PDF]
Academic Emergency Medicine, 2007 In part 1 of this series, the authors describe the importance of incomplete data in clinical research, and provide a conceptual framework for handling incomplete data by describing typical mechanisms and patterns of censoring, and detailing a variety of relatively simple methods and their limitations.
Craig D. Newgard, Jason S. Haukoos
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Improved imputation of summary statistics for admixed populations [PDF]
, 2017 AbstractMotivationSummary statistics imputation can be used to infer association summary statistics of an already conducted, genotype-based meta-analysis to higher ge-nomic resolution. This is typically needed when genotype imputation is not feasible for some cohorts.
Sina Rüeger +2 more
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JMIR Medical InformaticsBackgroundMissing data are a common challenge in electronic health record (EHR)–based prediction modeling. Traditional imputation methods may not suit prediction or machine learning models, and real-world use requires workflows that are implementable for
Jean Digitale +4 more
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Statistical Inference for Chi-square Statistics or F-Statistics Based on Multiple Imputation [PDF]
Missing data is a common issue in medical, psychiatry, and social studies. In literature, Multiple Imputation (MI) was proposed to multiply impute datasets and combine analysis results from imputed datasets for statistical inference using Rubin's rule. However, Rubin's rule only works for combined inference on statistical tests with point and variance ...
Binhuan Wang, Yixin Fang, Man Jin
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An empirical evaluation of imputation accuracy for association statistics reveals increased type-I error rates in genome-wide associations [PDF]
BMC Genetics, 2011 Background Genome wide association studies (GWAS) are becoming the approach of choice to identify genetic determinants of complex phenotypes and common diseases.
Pereira Alexandre C +4 more
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Imputation of Municipal Statistics Data
Vestnik NSUEM, 2020 Tatyana Skripkina
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