Results 31 to 40 of about 175,566 (257)
The problem of missingness in observational data is ubiquitous. When the confounders are missing at random, multiple imputation is commonly used; however, the method requires congeniality conditions for valid inferences, which may not be satisfied when ...
Corder Nathan, Yang Shu
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The ability of different imputation methods for missing values in mental measurement questionnaires
Background Incomplete data are of particular important influence in mental measurement questionnaires. Most experts, however, mostly focus on clinical trials and cohort studies and generally pay less attention to this deficiency. We aim is to compare the
Xueying Xu +5 more
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ABSTRACT Background Chronic micro‐inflammation in patients with end‐stage renal disease (ESRD) is a significant driver of cardiovascular complications and diminished quality of life. While standard hemodialysis (SHD) effectively manages small‐molecule clearance, its ability to remove medium‐to‐large uremic toxins—the primary catalysts of systemic ...
Hongwei Zuo +5 more
wiley +1 more source
BackgroundMissing data is a common nuisance in eHealth research: it is hard to prevent and may invalidate research findings. ObjectiveIn this paper several statistical approaches to data “missingness” are discussed and tested in a simulation ...
Blankers, Matthijs +2 more
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Multiple imputation of maritime search and rescue data at multiple missing patterns.
Based on the missing situation and actual needs of maritime search and rescue data, multiple imputation methods were used to construct complete data sets under different missing patterns.
Guobo Wang +4 more
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Design and analysis strategies for robust microbiome ageing research
The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
wiley +1 more source
Proper handling of missing data is a necessity for all data driven research. Multiple imputation is considered as a superior approach to handle missing data.
Johané Nienkemper-Swanepoel
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A Comparative Study on Missing Value Imputation Techniques in Machine Learning [PDF]
Handling missing values is a crucial step in data preprocessing, as incomplete data can significantly impact model performance and overall data integrity.
Meng Haoyu
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BackgroundA lifelogs-based wellness index (LWI) is a function for calculating wellness scores based on health behavior lifelogs (eg, daily walking steps and sleep times collected via a smartwatch).
Kim, Ki-Hun, Kim, Kwang-Jae
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Peripheral lysosomes recruit PLEKHG3 to focal adhesions and restrain protrusion dynamics
Proximity‐dependent labeling at the LAMTOR complex revealed the Rho GEF PLEKHG3 as a lysosome‐proximal protein directing the study toward the influence of lysosome positioning on actin dynamics and cell motility. We show that PLEKHG3 colocalizes with lysosomes at focal adhesion sites and observe that forced peripheral dispersion of lysosomes hinders ...
Rainer Ettelt +8 more
wiley +1 more source

