Results 41 to 50 of about 199,171 (299)
Multiple imputation methods for bivariate outcomes in cluster randomised trials. [PDF]
, 2016 Missing observations are common in cluster randomised trials. The problem is exacerbated when modelling bivariate outcomes jointly, as the proportion of complete cases is often considerably smaller than the proportion having either of the outcomes fully ...
DiazOrdaz, K +3 more
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A unifying framework for summary statistic imputation [PDF]
, 2018 AbstractImputation has been widely utilized to aid and interpret the results of Genome-Wide Association Studies(GWAS). Imputation can increase the power to identify associations when the causal variant was not directly observed or typed in the GWAS. There are two broad classes of methods for imputation.
Wu, Yue +2 more
openaire +1 more source
Recovering High‐Quality Host Genomes from Gut Metagenomic Data through Genotype Imputation
Advanced Genetics, 2022 Metagenomic datasets of host‐associated microbial communities often contain host DNA that is usually discarded because the amount of data is too low for accurate host genetic analyses.
Sofia Marcos +3 more
doaj +1 more source
ImputeGAN: Generative Adversarial Network for Multivariate Time Series Imputation
Entropy, 2023 Since missing values in multivariate time series data are inevitable, many researchers have come up with methods to deal with the missing data. These include case deletion methods, statistics-based imputation methods, and machine learning-based ...
Rui Qin, Yong Wang
doaj +1 more source
PLoS Genetics, 2022 Transcriptome wide association studies (TWAS) can be used as a powerful method to identify and interpret the underlying biological mechanisms behind GWAS by mapping gene expression levels with phenotypes.
Andrew E Liu, Hyun Min Kang
doaj +1 more source
Imputation and system modeling of acid-base state parameters for different groups of patients [PDF]
Научно-технический вестник информационных технологий, механики и оптики, 2022 The paper investigated the possibility of correct replacement of missing values in sets of acid-base state in the artery and vein in different groups of patients with different outcomes of the disease: “discharged”, “died”, “transferred to another ...
Dmitry I. Kurapeev +3 more
doaj +1 more source
Statistical Matching using Fractional Imputation
, 2015 Statistical matching is a technique for integrating two or more data sets when information available for matching records for individual participants across data sets is incomplete. Statistical matching can be viewed as a missing data problem where a researcher wants to perform a joint analysis of variables that are never jointly observed.
Kim, Jae Kwang +2 more
openaire +3 more sources
Briefings in Bioinformatics, 2021 Abstract Missing values are common in high-throughput mass spectrometry data. Two strategies are available to address missing values: (i) eliminate or impute the missing values and apply statistical methods that require complete data and (ii) use statistical methods that specifically account for missing values without imputation ...
Sandra Taylor +3 more
openaire +4 more sources
Bayesian correction for covariate measurement error: a frequentist evaluation and comparison with regression calibration [PDF]
, 2016 Bayesian approaches for handling covariate measurement error are well established, and yet arguably are still relatively little used by researchers. For some this is likely due to unfamiliarity or disagreement with the Bayesian inferential paradigm.
Bartlett, Jonathan W., Keogh, Ruth H.
core +2 more sources
Identifying and correcting for misspecifications in GWAS summary statistics and polygenic scores
HGG Advances, 2022 Summary: Publicly available genome-wide association studies (GWAS) summary statistics exhibit uneven quality, which can impact the validity of follow-up analyses.
Florian Privé +3 more
doaj +1 more source