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Items: 1 to 20 of 135

1.
2.

Binary variable multiple-model multiple imputation to address missing data mechanism uncertainty: application to a smoking cessation trial.

Siddique J, Harel O, Crespi CM, Hedeker D.

Stat Med. 2014 Jul 30;33(17):3013-28. doi: 10.1002/sim.6137. Epub 2014 Mar 17.

3.

A multiple imputation approach for MNAR mechanisms compatible with Heckman's model.

Galimard JE, Chevret S, Protopopescu C, Resche-Rigon M.

Stat Med. 2016 Jul 30;35(17):2907-20. doi: 10.1002/sim.6902. Epub 2016 Feb 18.

PMID:
26893215
4.

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.

5.

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
6.

A multiple imputation strategy for incomplete longitudinal data.

Landrum MB, Becker MP.

Stat Med. 2001 Sep 15-30;20(17-18):2741-60.

PMID:
11523080
7.

Multiple imputation approaches for the analysis of dichotomized responses in longitudinal studies with missing data.

Lu K, Jiang L, Tsiatis AA.

Biometrics. 2010 Dec;66(4):1202-8. doi: 10.1111/j.1541-0420.2010.01405.x.

8.

Treatment of nonignorable missing data when modeling unobserved heterogeneity with finite mixture models.

Lehmann T, Schlattmann P.

Biom J. 2017 Jan;59(1):159-171. doi: 10.1002/bimj.201500037. Epub 2016 Nov 2.

PMID:
27804147
9.

Multiple imputation of missing values was not necessary before performing a longitudinal mixed-model analysis.

Twisk J, de Boer M, de Vente W, Heymans M.

J Clin Epidemiol. 2013 Sep;66(9):1022-8. doi: 10.1016/j.jclinepi.2013.03.017. Epub 2013 Jun 21.

PMID:
23790725
10.

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. Epub 2006 Jul 11. Review.

PMID:
16980149
11.

Missing data and multiple imputation in clinical epidemiological research.

Pedersen AB, Mikkelsen EM, Cronin-Fenton D, Kristensen NR, Pham TM, Pedersen L, Petersen I.

Clin Epidemiol. 2017 Mar 15;9:157-166. doi: 10.2147/CLEP.S129785. eCollection 2017.

12.

Dealing with missing data in the Center for Epidemiologic Studies Depression self-report scale: a study based on the French E3N cohort.

Resseguier N, Verdoux H, Giorgi R, Clavel-Chapelon F, Paoletti X.

BMC Med Res Methodol. 2013 Feb 21;13:28. doi: 10.1186/1471-2288-13-28.

13.

Combining multiple imputation and meta-analysis with individual participant data.

Burgess S, White IR, Resche-Rigon M, Wood AM.

Stat Med. 2013 Nov 20;32(26):4499-514. doi: 10.1002/sim.5844. Epub 2013 May 24.

14.

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.

15.

How to deal with missing longitudinal data in cost of illness analysis in Alzheimer's disease-suggestions from the GERAS observational study.

Belger M, Haro JM, Reed C, Happich M, Kahle-Wrobleski K, Argimon JM, Bruno G, Dodel R, Jones RW, Vellas B, Wimo A.

BMC Med Res Methodol. 2016 Jul 18;16:83. doi: 10.1186/s12874-016-0188-1.

16.

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.

17.

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.

18.

Evaluating the impact of imputations for missing participant outcome data in a network meta-analysis.

Spineli LM, Higgins JP, Cipriani A, Leucht S, Salanti G.

Clin Trials. 2013;10(3):378-88. doi: 10.1177/1740774512470317. Epub 2013 Jan 15.

PMID:
23321265
19.

An analytic method for the placebo-based pattern-mixture model.

Lu K.

Stat Med. 2014 Mar 30;33(7):1134-45. doi: 10.1002/sim.6008. Epub 2013 Oct 9.

PMID:
24122822
20.

Using an Approximate Bayesian Bootstrap to Multiply Impute Nonignorable Missing Data.

Siddique J, Belin TR.

Comput Stat Data Anal. 2008 Dec 15;53(2):405-415.

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