Results 291 to 300 of about 287,343 (334)

Missing Data Imputation (35 Patients)

2012
In clinical research missing data are common, and compared to demographics, clinical research produces generally smaller files, making a few missing data more of a problem than it is with demographic files. As an example, a 35 patient data file of 3 variables consists of 3 × 35 = 105 values if the data are complete.
Ton J. Cleophas, Aeilko H. Zwinderman
openaire   +1 more source

Imputing Missing Network Data

2016
Missing data on network ties is a fundamental problem for network analyses. The biases induced by missing edge data, even when missing completely at random (MCAR), are widely acknowledged (Kossinets, 2006; Huisman & Steglich, 2008; Huisman, 2009). Although model based techniques for missing network data are quite promising, they are not available ...
Krause, Robert, Huisman, Mark, Steglich, Christian, Snijders, T.A.B.   +3 more
openaire   +1 more source

SP0187 Imputation of Missing Data

Annals of the Rheumatic Diseases, 2014
Missing data is very common problem in epidemiological studies, and this problem can affect both the accuracy and the precision of any estimates generated from the study data. The magnitude of the problem will depend to some extent on whether the missing data refer to outcomes, exposures or confounders.
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Missing Data Imputation and Analysis

2011
Missing data are a common occurrence in scientific research and in our daily lives. In a survey, a lack of response constitutes missing data. In clinical trials, missing data can be caused by a patient’s refusal to continue in a study, treatment failures, adverse events, or patient relocations.
openaire   +1 more source

An overview of real‐world data sources for oncology and considerations for research

Ca-A Cancer Journal for Clinicians, 2022
Lynne Penberthy, Donna R Rivera, Jennifer L Lund, Msph   +2 more
exaly  

Imputation of the Missing Data

2013
We may consider the existence of missing observations as unimportant, considering that the risk of misunderstanding is negligible. The surveyor assumes some model that allows adequately explaining the variable of interest. In such cases, we are able to predict the unknown values and to plug them into some estimator.
openaire   +1 more source

BAYESIAN IMPUTATION FOR MISSING DATA

Advances and Applications in Statistics, 2022
Azman A. Nads, Daisy Lou L. Polestico
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Innovations in research and clinical care using patient‐generated health data

Ca-A Cancer Journal for Clinicians, 2020
H S L Jim, Aasha I Hoogland, Naomi C Brownstein   +2 more
exaly  

Imputation of Missing Network Data

2017
Huisman, Mark, Krause, Robert W.
openaire   +1 more source

Missing Data, Imputation of

2022
openaire   +1 more source