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Items: 1 to 20 of 142

1.

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.

2.
3.

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
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.

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.

7.
8.

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.

9.

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.

10.

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
11.

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
12.

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.

13.

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.

14.

Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst's Perspective.

Schafer JL, Olsen MK.

Multivariate Behav Res. 1998 Oct 1;33(4):545-71. doi: 10.1207/s15327906mbr3304_5.

PMID:
26753828
15.

Missing data in the American College of Surgeons National Surgical Quality Improvement Program are not missing at random: implications and potential impact on quality assessments.

Hamilton BH, Ko CY, Richards K, Hall BL.

J Am Coll Surg. 2010 Feb;210(2):125-139.e2. doi: 10.1016/j.jamcollsurg.2009.10.021.

PMID:
20113932
16.

Multiple imputation for missing data: fully conditional specification versus multivariate normal imputation.

Lee KJ, Carlin JB.

Am J Epidemiol. 2010 Mar 1;171(5):624-32. doi: 10.1093/aje/kwp425. Epub 2010 Jan 27.

PMID:
20106935
17.

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
18.

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
19.

Imputation-based strategies for clinical trial longitudinal data with nonignorable missing values.

Yang X, Li J, Shoptaw S.

Stat Med. 2008 Jul 10;27(15):2826-49. doi: 10.1002/sim.3111.

20.

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.

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