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Items: 1 to 20 of 160

1.

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.

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Cumulative sojourn time in longitudinal studies: a sequential imputation method to handle missing health state data due to dropout.

Li X, Liu J, Duan N, Jiang H, Girgis R, Lieberman J.

Stat Med. 2014 May 30;33(12):2030-47.

PMID:
24918241
5.
6.

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. doi: 10.1016/j.annepidem.2013.10.007. Epub 2013 Oct 18.

PMID:
24210708
7.

A structured framework for assessing sensitivity to missing data assumptions in longitudinal clinical trials.

Mallinckrodt CH, Lin Q, Molenberghs M.

Pharm Stat. 2013 Jan-Feb;12(1):1-6. doi: 10.1002/pst.1547. Epub 2012 Nov 28.

PMID:
23193075
8.

How should we deal with missing data in clinical trials involving Alzheimer's disease patients?

Coley N, Gardette V, Cantet C, Gillette-Guyonnet S, Nourhashemi F, Vellas B, Andrieu S.

Curr Alzheimer Res. 2011 Jun;8(4):421-33.

PMID:
21244348
9.

Estimating the effect of multiple imputation on incomplete longitudinal data with application to a randomized clinical study.

Fong DY, Rai SN, Lam KS.

J Biopharm Stat. 2013;23(5):1004-22. doi: 10.1080/10543406.2013.813514.

PMID:
23957512
10.

Markov transition models for binary repeated measures with ignorable and nonignorable missing values.

Xiaowei Yang, Shoptaw S, Kun Nie, Juanmei Liu, Belin TR.

Stat Methods Med Res. 2007 Aug;16(4):347-64. Review.

PMID:
17715161
11.

The Missing=Smoking Assumption: A Fallacy in Internet-Based Smoking Cessation Trials?

Blankers M, Smit ES, van der Pol P, de Vries H, Hoving C, van Laar M.

Nicotine Tob Res. 2016 Jan;18(1):25-33. doi: 10.1093/ntr/ntv055. Epub 2015 Mar 5.

PMID:
25744969
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14.

Comparison of imputation and modelling methods in the analysis of a physical activity trial with missing outcomes.

Wood AM, White IR, Hillsdon M, Carpenter J.

Int J Epidemiol. 2005 Feb;34(1):89-99. Epub 2004 Aug 27.

PMID:
15333619
15.

Comparison of alternative strategies for analysis of longitudinal trials with dropouts.

Liu G, Gould AL.

J Biopharm Stat. 2002 May;12(2):207-26.

PMID:
12413241
16.

Analysis of longitudinal binary data with missing data due to dropouts.

Ali MW, Talukder E.

J Biopharm Stat. 2005;15(6):993-1007.

PMID:
16279357
17.
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.

A review of the handling of missing longitudinal outcome data in clinical trials.

Powney M, Williamson P, Kirkham J, Kolamunnage-Dona R.

Trials. 2014 Jun 19;15:237. doi: 10.1186/1745-6215-15-237. Review.

20.

Analysis of longitudinal clinical trials with missing data using multiple imputation in conjunction with robust regression.

Mehrotra DV, Li X, Liu J, Lu K.

Biometrics. 2012 Dec;68(4):1250-9. doi: 10.1111/j.1541-0420.2012.01780.x. Epub 2012 Sep 20.

PMID:
22994905

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