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Items: 1 to 20 of 98

1.

Multiple Imputation For Combined-Survey Estimation With Incomplete Regressors In One But Not Both Surveys.

Rendall MS, Ghosh-Dastidar B, Weden MM, Baker EH, Nazarov Z.

Sociol Methods Res. 2013 Nov 1;42(4). doi: 10.1177/0049124113502947.

2.

Is using multiple imputation better than complete case analysis for estimating a prevalence (risk) difference in randomized controlled trials when binary outcome observations are missing?

Mukaka M, White SA, Terlouw DJ, Mwapasa V, Kalilani-Phiri L, Faragher EB.

Trials. 2016 Jul 22;17:341. doi: 10.1186/s13063-016-1473-3.

3.

Multiple imputation for non-response when estimating HIV prevalence using survey data.

Chinomona A, Mwambi H.

BMC Public Health. 2015 Oct 16;15:1059. doi: 10.1186/s12889-015-2390-1.

4.

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.

5.

Bias and Precision of the "Multiple Imputation, Then Deletion" Method for Dealing With Missing Outcome Data.

Sullivan TR, Salter AB, Ryan P, Lee KJ.

Am J Epidemiol. 2015 Sep 15;182(6):528-34. doi: 10.1093/aje/kwv100. Epub 2015 Sep 2.

PMID:
26337075
6.

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.

PMID:
20842622
7.

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.

8.

Analysis of partially observed clustered data using generalized estimating equations and multiple imputation.

Aloisio KM, Swanson SA, Micali N, Field A, Horton NJ.

Stata J. 2014 Oct 1;14(4):863-883.

9.

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.

10.

Assessment of predictive performance in incomplete data by combining internal validation and multiple imputation.

Wahl S, Boulesteix AL, Zierer A, Thorand B, Avan de Wiel M.

BMC Med Res Methodol. 2016 Oct 26;16(1):144. Erratum in: BMC Med Res Methodol. 2016 Dec 5;16(1):170.

11.

Multiple imputation to deal with missing EQ-5D-3L data: Should we impute individual domains or the actual index?

Simons CL, Rivero-Arias O, Yu LM, Simon J.

Qual Life Res. 2015 Apr;24(4):805-15. doi: 10.1007/s11136-014-0837-y. Epub 2014 Dec 4.

PMID:
25471286
12.

Multiple Imputation to Deal with Missing Clinical Data in Rheumatologic Surveys: an Application in the WHO-ILAR COPCORD Study in Iran.

Mirmohammadkhani M, Foroushani AR, Davatchi F, Mohammad K, Jamshidi A, Banihashemi AT, Naieni KH.

Iran J Public Health. 2012;41(1):87-95. Epub 2012 Jan 31.

13.

Sensitivity analysis after multiple imputation under missing at random: a weighting approach.

Carpenter JR, Kenward MG, White IR.

Stat Methods Med Res. 2007 Jun;16(3):259-75.

PMID:
17621471
14.

Multiple imputation of missing dual-energy X-ray absorptiometry data in the National Health and Nutrition Examination Survey.

Schenker N, Borrud LG, Burt VL, Curtin LR, Flegal KM, Hughes J, Johnson CL, Looker AC, Mirel L.

Stat Med. 2011 Feb 10;30(3):260-76. doi: 10.1002/sim.4080. Epub 2010 Nov 30.

PMID:
21213343
15.

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.

16.

Simulation-based study comparing multiple imputation methods for non-monotone missing ordinal data in longitudinal settings.

Donneau AF, Mauer M, Lambert P, Molenberghs G, Albert A.

J Biopharm Stat. 2015;25(3):570-601. doi: 10.1080/10543406.2014.920864.

PMID:
24905056
17.

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.

18.

Improving upon the efficiency of complete case analysis when covariates are MNAR.

Bartlett JW, Carpenter JR, Tilling K, Vansteelandt S.

Biostatistics. 2014 Oct;15(4):719-30. doi: 10.1093/biostatistics/kxu023. Epub 2014 Jun 6. Erratum in: Biostatistics. 2015 Jan;16(1):205.

19.

[Multiple imputation of missing at random data: General points and presentation of a Monte-Carlo method].

Cottrell G, Cot M, Mary JY.

Rev Epidemiol Sante Publique. 2009 Oct;57(5):361-72. doi: 10.1016/j.respe.2009.04.011. Epub 2009 Aug 11. French.

PMID:
19674855
20.

The rise of multiple imputation: a review of the reporting and implementation of the method in medical research.

Hayati Rezvan P, Lee KJ, Simpson JA.

BMC Med Res Methodol. 2015 Apr 7;15:30. doi: 10.1186/s12874-015-0022-1. Review.

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