Results 61 to 70 of about 375,599 (291)
Application of Multiple imputation in Analysis of missing data in a study of Health-related quality of life [PDF]
, 2011 When a new treatment has similar efficacy compared to standard therapy in medical or social studies, the health-related quality of life (HRQL) becomes the main concern of health care professionals and can be the basis for making a decision in patient ...
Zhu, Chunming
core +1 more source
Five‐Year Disease Progression in Synuclein Seeding Positive Sporadic Parkinson's Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To provide a comprehensive description of disease progression in synuclein seeding assay (SAA) positive sporadic Parkinson Disease participants, using Neuronal Synuclein Disease integrated biological and functional impairment staging framework.
Paulina Gonzalez‐Latapi +19 more
wiley +1 more source
BiologySingle-cell transcriptomics (scRNA-seq) is revolutionizing biological research, yet it faces challenges such as inefficient transcript capture and noise. To address these challenges, methods like neighbor averaging or graph diffusion are used.
Padron-Manrique Cristian +8 more
doaj +1 more source
When to Impute? Imputation before and during cross-validation
CoRR, 2020 Cross-validation (CV) is a technique used to estimate generalization error for prediction models. For pipeline modeling algorithms (i.e. modeling procedures with multiple steps), it has been recommended the entire sequence of steps be carried out during each replicate of CV to mimic the application of the entire pipeline to an external testing set ...
Byron C. Jaeger +2 more
openaire +2 more sources
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective In multiple sclerosis, the optimal time for deploying a therapeutic intervention is before the central nervous system is damaged; given the success of trials treating the earliest stage of MS, the radiologically isolated syndrome, developing primary prevention strategies is an important next challenge.
Amy W. Laitinen +7 more
wiley +1 more source
Imputation of truncated p-values for meta-analysis methods and its genomic application
, 2014 Microarray analysis to monitor expression activities in thousands of genes simultaneously has become routine in biomedical research during the past decade.
Ding, Ying +5 more
core +1 more source
Can k-NN imputation improve the performance of C4.5 with small software project data sets? A comparative evaluation [PDF]
, 2008 Missing data is a widespread problem that can affect the ability to use data to construct effective prediction systems. We investigate a common machine learning technique that can tolerate missing values, namely C4.5, to predict cost using six real world
Albrecht +60 more
core +1 more source
Multiply-Imputed Synthetic Data: Advice to the Imputer [PDF]
Journal of Official Statistics, 2017 Abstract Several statistical agencies have started to use multiply-imputed synthetic microdata to create public-use data in major surveys. The purpose of doing this is to protect the confidentiality of respondents’ identities and sensitive attributes, while allowing standard complete-data analyses of microdata.
Loong, Bronwyn, Rubin, Donald B
openaire +2 more sources
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Onasemnogene abeparvovec (OA) is an AAV9‐based gene therapy for spinal muscular atrophy type I (SMA I). Real‐world outcomes show increased response variability compared to clinical trials, and follow‐up data beyond 12–18 months are limited.
Marika Pane +43 more
wiley +1 more source
An Empirical Comparison of Multiple Imputation Methods for Categorical Data
, 2016 Multiple imputation is a common approach for dealing with missing values in statistical databases. The imputer fills in missing values with draws from predictive models estimated from the observed data, resulting in multiple, completed versions of the ...
Akande, Olanrewaju +2 more
core +2 more sources