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Journal Abstract Search
571 related items for 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 [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 [Abstract] [Full Text] [Related]
11. The multiple imputation method: a case study involving secondary data analysis. Walani SR, Cleland CM. Nurse Res; 2015 May 03; 22(5):13-9. PubMed ID: 25976532 [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 03; 22(2):. PubMed ID: 28425202 [Abstract] [Full Text] [Related]
15. Multiple imputation for missing data. Patrician PA. Res Nurs Health; 2002 Feb 03; 25(1):76-84. PubMed ID: 11807922 [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 09; 17(1):2. PubMed ID: 28068910 [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 06; 21(1):97. PubMed ID: 33952189 [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 05; 15(1):346. PubMed ID: 25371041 [Abstract] [Full Text] [Related] Page: [Next] [New Search]