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Items: 1 to 20 of 403


Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study.

Marshall A, Altman DG, Holder RL.

BMC Med Res Methodol. 2010 Dec 31;10:112. doi: 10.1186/1471-2288-10-112.


Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study.

Marshall A, Altman DG, Royston P, Holder RL.

BMC Med Res Methodol. 2010 Jan 19;10:7. doi: 10.1186/1471-2288-10-7.


Comparison of methods for handling missing data on immunohistochemical markers in survival analysis of breast cancer.

Ali AM, Dawson SJ, Blows FM, Provenzano E, Ellis IO, Baglietto L, Huntsman D, Caldas C, Pharoah PD.

Br J Cancer. 2011 Feb 15;104(4):693-9. doi: 10.1038/sj.bjc.6606078. Epub 2011 Jan 25.


Imputation strategies for missing binary outcomes in cluster randomized trials.

Ma J, Akhtar-Danesh N, Dolovich L, Thabane L; CHAT investigators.

BMC Med Res Methodol. 2011 Feb 16;11:18. doi: 10.1186/1471-2288-11-18.


Imputation of missing values of tumour stage in population-based cancer registration.

Eisemann N, Waldmann A, Katalinic A.

BMC Med Res Methodol. 2011 Sep 19;11:129. doi: 10.1186/1471-2288-11-129.


Estimating excess hazard ratios and net survival when covariate data are missing: strategies for multiple imputation.

Falcaro M, Nur U, Rachet B, Carpenter JR.

Epidemiology. 2015 May;26(3):421-8. doi: 10.1097/EDE.0000000000000283.


Recovery of information from multiple imputation: a simulation study.

Lee KJ, Carlin JB.

Emerg Themes Epidemiol. 2012 Jun 13;9(1):3. doi: 10.1186/1742-7622-9-3.


The performance of multiple imputation for missing covariate data within the context of regression relative survival analysis.

Giorgi R, Belot A, Gaudart J, Launoy G; French Network of Cancer Registries FRANCIM.

Stat Med. 2008 Dec 30;27(30):6310-31. doi: 10.1002/sim.3476.


Handling missing data in matched case-control studies using multiple imputation.

Seaman SR, Keogh RH.

Biometrics. 2015 Dec;71(4):1150-9. doi: 10.1111/biom.12358. Epub 2015 Aug 3.


Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values.

White IR, Carlin JB.

Stat Med. 2010 Dec 10;29(28):2920-31. doi: 10.1002/sim.3944.


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 25;17(1):114. doi: 10.1186/s12874-017-0372-y.


Outcome-sensitive multiple imputation: a simulation study.

Kontopantelis E, White IR, Sperrin M, Buchan I.

BMC Med Res Methodol. 2017 Jan 9;17(1):2. doi: 10.1186/s12874-016-0281-5.


Multiple imputation methods for handling missing data in cost-effectiveness analyses that use data from hierarchical studies: an application to cluster randomized trials.

Gomes M, Díaz-Ordaz K, Grieve R, Kenward MG.

Med Decis Making. 2013 Nov;33(8):1051-63. doi: 10.1177/0272989X13492203. Epub 2013 Aug 1.


Evaluation of a weighting approach for performing sensitivity analysis after multiple imputation.

Rezvan PH, White IR, Lee KJ, Carlin JB, Simpson JA.

BMC Med Res Methodol. 2015 Oct 13;15:83. doi: 10.1186/s12874-015-0074-2.


Missing data in a multi-item instrument were best handled by multiple imputation at the item score level.

Eekhout I, de Vet HC, Twisk JW, Brand JP, de Boer MR, Heymans MW.

J Clin Epidemiol. 2014 Mar;67(3):335-42. doi: 10.1016/j.jclinepi.2013.09.009. Epub 2013 Dec 2.


Missing data approaches in eHealth research: simulation study and a tutorial for nonmathematically inclined researchers.

Blankers M, Koeter MW, Schippers GM.

J Med Internet Res. 2010 Dec 19;12(5):e54. doi: 10.2196/jmir.1448.


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 3;17(1):402.


The use of complete-case and multiple imputation-based analyses in molecular epidemiology studies that assess interaction effects.

Desai M, Esserman DA, Gammon MD, Terry MB.

Epidemiol Perspect Innov. 2011 Oct 6;8(1):5. doi: 10.1186/1742-5573-8-5.


Multiple imputation strategies for zero-inflated cost data in economic evaluations: which method works best?

MacNeil Vroomen J, Eekhout I, Dijkgraaf MG, van Hout H, de Rooij SE, Heymans MW, Bosmans JE.

Eur J Health Econ. 2016 Nov;17(8):939-950. Epub 2015 Oct 23.

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