565 related articles for article (PubMed ID: 11927199)
1. Attrition in longitudinal studies. How to deal with missing data.
Twisk J; de Vente W
J Clin Epidemiol; 2002 Apr; 55(4):329-37. PubMed ID: 11927199
[TBL] [Abstract][Full Text] [Related]
2. Missing data in longitudinal studies: cross-sectional multiple imputation provides similar estimates to full-information maximum likelihood.
Ferro MA
Ann Epidemiol; 2014 Jan; 24(1):75-7. PubMed ID: 24210708
[TBL] [Abstract][Full Text] [Related]
3. Multiple imputation for non-response when estimating HIV prevalence using survey data.
Chinomona A; Mwambi H
BMC Public Health; 2015 Oct; 15():1059. PubMed ID: 26475303
[TBL] [Abstract][Full Text] [Related]
4. A comparison of multiple imputation methods for missing data in longitudinal studies.
Huque MH; Carlin JB; Simpson JA; Lee KJ
BMC Med Res Methodol; 2018 Dec; 18(1):168. PubMed ID: 30541455
[TBL] [Abstract][Full Text] [Related]
5. Review: a gentle introduction to imputation of missing values.
Donders AR; van der Heijden GJ; Stijnen T; Moons KG
J Clin Epidemiol; 2006 Oct; 59(10):1087-91. PubMed ID: 16980149
[TBL] [Abstract][Full Text] [Related]
6. Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework.
Voillet V; Besse P; Liaubet L; San Cristobal M; González I
BMC Bioinformatics; 2016 Oct; 17(1):402. PubMed ID: 27716030
[TBL] [Abstract][Full Text] [Related]
7. A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear association with time: a simulation study.
De Silva AP; Moreno-Betancur M; De Livera AM; Lee KJ; Simpson JA
BMC Med Res Methodol; 2017 Jul; 17(1):114. PubMed ID: 28743256
[TBL] [Abstract][Full Text] [Related]
8. Imputing cross-sectional missing data: comparison of common techniques.
Hawthorne G; Elliott P
Aust N Z J Psychiatry; 2005 Jul; 39(7):583-90. PubMed ID: 15996139
[TBL] [Abstract][Full Text] [Related]
9. Analysis of the benefits of a Mediterranean diet in the GISSI-Prevenzione study: a case study in imputation of missing values from repeated measurements.
Barzi F; Woodward M; Marfisi RM; Tognoni G; Marchioli R;
Eur J Epidemiol; 2006; 21(1):15-24. PubMed ID: 16450202
[TBL] [Abstract][Full Text] [Related]
10. Guidelines for multiple imputations in repeated measurements with time-dependent covariates: a case study.
Tan FES; Jolani S; Verbeek H
J Clin Epidemiol; 2018 Oct; 102():107-114. PubMed ID: 29964148
[TBL] [Abstract][Full Text] [Related]
11. The multiple imputation method: a case study involving secondary data analysis.
Walani SR; Cleland CM
Nurse Res; 2015 May; 22(5):13-9. PubMed ID: 25976532
[TBL] [Abstract][Full Text] [Related]
12. Examining solutions to missing data in longitudinal nursing research.
Roberts MB; Sullivan MC; Winchester SB
J Spec Pediatr Nurs; 2017 Apr; 22(2):. PubMed ID: 28425202
[TBL] [Abstract][Full Text] [Related]
13. Dealing with missing covariates in epidemiologic studies: a comparison between multiple imputation and a full Bayesian approach.
Erler NS; Rizopoulos D; Rosmalen Jv; Jaddoe VW; Franco OH; Lesaffre EM
Stat Med; 2016 Jul; 35(17):2955-74. PubMed ID: 27042954
[TBL] [Abstract][Full Text] [Related]
14. Imputing missing repeated measures data: how should we proceed?
Elliott P; Hawthorne G
Aust N Z J Psychiatry; 2005 Jul; 39(7):575-82. PubMed ID: 15996138
[TBL] [Abstract][Full Text] [Related]
15. Multiple imputation for missing data.
Patrician PA
Res Nurs Health; 2002 Feb; 25(1):76-84. PubMed ID: 11807922
[TBL] [Abstract][Full Text] [Related]
16. Outcome-sensitive multiple imputation: a simulation study.
Kontopantelis E; White IR; Sperrin M; Buchan I
BMC Med Res Methodol; 2017 Jan; 17(1):2. PubMed ID: 28068910
[TBL] [Abstract][Full Text] [Related]
17. Methods for handling missing data in serially sampled sputum specimens for mycobacterial culture conversion calculation.
Malatesta S; Weir IR; Weber SE; Bouton TC; Carney T; Theron D; Myers B; Horsburgh CR; Warren RM; Jacobson KR; White LF
BMC Med Res Methodol; 2022 Nov; 22(1):297. PubMed ID: 36402979
[TBL] [Abstract][Full Text] [Related]
18. Review and evaluation of imputation methods for multivariate longitudinal data with mixed-type incomplete variables.
Cao Y; Allore H; Vander Wyk B; Gutman R
Stat Med; 2022 Dec; 41(30):5844-5876. PubMed ID: 36220138
[TBL] [Abstract][Full Text] [Related]
19. Dealing with missing delirium assessments in prospective clinical studies of the critically ill: a simulation study and reanalysis of two delirium studies.
Raman R; Chen W; Harhay MO; Thompson JL; Ely EW; Pandharipande PP; Patel MB
BMC Med Res Methodol; 2021 May; 21(1):97. PubMed ID: 33952189
[TBL] [Abstract][Full Text] [Related]
20. Missing value imputation in high-dimensional phenomic data: imputable or not, and how?
Liao SG; Lin Y; Kang DD; Chandra D; Bon J; Kaminski N; Sciurba FC; Tseng GC
BMC Bioinformatics; 2014 Nov; 15(1):346. PubMed ID: 25371041
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
