Results 11 to 20 of about 762,527 (191)

On Structural Equation Modeling with Data that are not Missing Completely at Random [PDF]

Psychometrika, 1987
A general latent variable model is given which includes the specification of a missing data mechanism. This framework allows for an elucidating discussion of existing general multivariate theory bearing on maximum likelihood estimation with missing data.
Muthén, Bengt, Kaplan, David, Hollis, Michael   +2 more
openaire   +2 more sources

Outcome-sensitive multiple imputation: a simulation study [PDF]

BMC Medical Research Methodology, 2017
Background Multiple imputation is frequently used to deal with missing data in healthcare research. Although it is known that the outcome should be included in the imputation model when imputing missing covariate values, it is not known whether it should
Evangelos Kontopantelis, Ian R. White, Matthew Sperrin, Iain Buchan   +3 more
doaj   +9 more sources

Missing data and multiple imputation in clinical epidemiological research [PDF]

Clinical Epidemiology, 2017
Alma B Pedersen,1 Ellen M Mikkelsen,1 Deirdre Cronin-Fenton,1 Nickolaj R Kristensen,1 Tra My Pham,2 Lars Pedersen,1 Irene Petersen1,2 1Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus N, Denmark; 2Department of Primary Care and ...
Pedersen AB, Mikkelsen EM, Cronin-Fenton D, Kristensen NR, Pham TM, Pedersen L, Petersen I   +6 more
doaj   +3 more sources

Missing data, part 2. Missing data mechanisms: Missing completely at random, missing at random, missing not at random, and why they matter

American Journal of Orthodontics and Dentofacial Orthopedics, 2022
Tra My Pham, Nikolaos Pandis, Ian R. White   +2 more
openaire   +4 more sources

Comparison of Imputation Methods for Missing Values in Longitudinal Data Under Missing Completely at Random (mcar) mechanism

African Journal of Applied Statistics, 2017
Dans cet article, nous comparons les performances des cinq (5) techniques d'imputation de valeurs sous les hypotheses de donnees manquantes de maniere completement aleatoirement (MCAR). La comparaison se fait la base du modele des Equations Generalisees d'Estimation (GEE) pour la base complete, le ceofficient de determination, de l'erreur quadratique ...
ANANI, Lotsi, ASIEDU, Louis, KATSEKPOR, Johnson   +2 more
openaire   +3 more sources

Comparative Study of Four Methods in Missing Value Imputations under Missing Completely at Random Mechanism

Open Journal of Statistics, 2014
In analyzing data from clinical trials and longitudinal studies, the issue of missing values is always a fundamental challenge since the missing data could introduce bias and lead to erroneous statistical inferences. To deal with this challenge, several imputation methods have been developed in the literature to handle missing values where the ...
Michikazu Nakai, Ding-Geng Chen, Kunihiro Nishimura, Yoshihiro Miyamoto   +3 more
openaire   +3 more sources

On summary measures analysis of the linear mixed effects model for repeated measures when data are not missing completely at random [PDF]

Statistics in Medicine, 1999
Subjects often drop out of longitudinal studies prematurely, yielding unbalanced data with unequal numbers of measures for each subject. A simple and convenient approach to analysis is to develop summary measures for each individual and then regress the summary measures on between-subject covariates.
Little, Roderick J. A., Raghunathan, Trivellore E.   +1 more
openaire   +3 more sources

Reconstruction of Missing Data Completely at Random for Trains Based on Improved GAN

Journal of Advanced Computational Intelligence and Intelligent Informatics
Reconstruction of missing data for heavy-haul trains is critical to ensuring safe train operation. However, existing generative model training methods require a complete dataset, making it difficult for them to address the issue of missing data completely at random.
Jing He, Xin Chen, Changfan Zhang
openaire   +2 more sources

Impact on Cronbach's alpha of simple treatment methods for missing data [PDF]

Tutorials in Quantitative Methods for Psychology, 2016
The scientific treatment of missing data has been the subject of research for nearly a century. Strangely, interest in missing data is quite new in the fields of educational science and psychology (Peugh & Enders, 2004; Schafer & Graham, 2002). It is now
Béland, Sébastien, Pichette, Francois, Jolani, S.   +2 more
doaj   +1 more source

Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity

BMC Medical Research Methodology, 2023
Background Detecting treatment effect heterogeneity is an important objective in cluster randomized trials and implementation research. While sample size procedures for testing the average treatment effect accounting for participant attrition assuming ...
Jiaqi Tong, Fan Li, Michael O. Harhay, Guangyu Tong   +3 more
doaj   +1 more source