Results 11 to 20 of about 764,403 (277)

Is cancer stage data missing completely at random? A report from a large population-based cohort of non-small cell lung cancer [PDF]

Frontiers in Oncology, 2023
IntroductionPopulation-based datasets are often used to estimate changes in utilization or outcomes of novel therapies. Inclusion or exclusion of unstaged patients may impact on interpretation of these studies.MethodsA large population-based dataset in ...
Andrew G. Robinson, Andrew G. Robinson, Paul Nguyen, Catherine L. Goldie, Matthew Jalink, Matthew Jalink, Timothy P. Hanna, Timothy P. Hanna, Timothy P. Hanna   +8 more
doaj   +2 more sources

Tests of Homoscedasticity, Normality, and Missing Completely at Random for Incomplete Multivariate Data [PDF]

Psychometrika, 2010
Test of homogeneity of covariances (or homoscedasticity) among several groups has many applications in statistical analysis. In the context of incomplete data analysis, tests of homoscedasticity among groups of cases with identical missing data patterns have been proposed to test whether data are missing completely at random (MCAR). These tests of MCAR
Mortaza Jamshidian, Siavash Jalal
openaire   +6 more sources

Comparison of methods to handle missing values in a binary index test in a diagnostic accuracy study – a simulation study [PDF]

BMC Medical Research Methodology
Background As there are no recommendations on handling missing values in a dichotomous index test of a diagnostic study, researchers often ignore missing values in the analysis or use simple methods.
Dennis Juljugin, Katharina Stahlmann, Antonia Zapf   +2 more
doaj   +2 more sources

Missingness mechanisms and generalizability of patient reported outcome measures in colorectal cancer survivors – assessing the reasonableness of the “missing completely at random” assumption [PDF]

BMC Medical Research Methodology
Background Patient-Reported Outcome Measures (PROM) provide important information, however, missing PROM data threaten the interpretability and generalizability of findings by introducing potential bias.
Johanne Dam Lyhne, Allan ‘Ben’ Smith, Lars Henrik Jensen, Torben Frøstrup Hansen, Lisbeth Frostholm, Signe Timm   +5 more
doaj   +2 more sources

A comparison of two approaches to implementing propensity score methods following multiple imputation

Epidemiology, Biostatistics and Public Health, 2022
Background. In observational research on causal effects, missing data and confounding are very common problems. Multiple imputation and propensity score methods have gained increasing interest as methods to deal with these, but despite their ...
Bas BL Penning de Vries, Rolf HH Groenwold   +1 more
doaj   +1 more source

Variational Bayesian Inference for Quantile Regression Models with Nonignorable Missing Data

Mathematics, 2023
Quantile regression models are remarkable structures for conducting regression analyses when the data are subject to missingness. Missing values occur because of various factors like missing completely at random, missing at random, or missing not at ...
Xiaoning Li, Mulati Tuerde, Xijian Hu
doaj   +1 more source

Multiple Imputation Ensembles (MIE) for dealing with missing data [PDF]

, 2020
Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation ...
A Farhangfar, AM Sefidian, B Schölkopf, C Cortes, CT Tran, DA Newman, DB Rubin, DB Rubin, DH Wolpert, EL Silva-Ramírez, GE Batista, GJ van der Heijden, H Gao, IH Witten, J Demšar, J Honaker, J Honaker, J Scheffer, JA Sterne, JL Schafer, JL Schafer, JR Quinlan, K Abayomi, KM Ting, L Breiman, L Breiman, L Rokach, M Fichman, M Khalilia, M Spratt, MA Klebanoff, MJ Azur, NJ Horton, PJ García-Laencina, PJ Kelly, PN Tan, RJ Little, S García, S Van Buuren, S Van Buuren, SS Chae, SS Choi, U Garciarena, V Vapnik, X Chen, Y Dong, Y Freund, Y He, Z Che, Z Liu   +49 more
core   +1 more source

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   +1 more source

A simplified eye-opener on managing missing data and in evaluation of non-response bias in medical research: a narrative review

Journal of the College of Community Physicians, 2018
Using correct methods for prevention, analysis and treatment of missing data is essential in preserving the validity of scientific research. In spite of this, issues related to missing data and non-response bias are found to be inadequately discussed in ...
P. K. B. Mahesh, Wasantha Gunathunga, Mahendra arnold, S. B. Munasinghe, Sinha De Silva   +4 more
doaj   +1 more source

Comprehensive analysis of missing data imputation in clinical time-series: challenges, risks, and practical solutions [PDF]

PeerJ Computer Science
Missing data in clinical time series is pervasive and decision-critical, arising from irregular sampling, workflow-driven measurement policies, sensor failures, and intervention-dependent monitoring.
Aasim Ayaz Wani, Fatima Abeer
doaj   +2 more sources