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Results: 1 to 20 of 109

Related Citations for PubMed (Select 23242384)

1.

Bayesian multiple imputation for missing multivariate longitudinal data from a Parkinson's disease clinical trial.

Luo S, Lawson AB, He B, Elm JJ, Tilley BC.

Stat Methods Med Res. 2012 Dec 12. [Epub ahead of print]

2.
3.

Robust Bayesian inference for multivariate longitudinal data by using normal/independent distributions.

Luo S, Ma J, Kieburtz KD.

Stat Med. 2013 Sep 30;32(22):3812-28. doi: 10.1002/sim.5778. Epub 2013 Mar 11.

4.

Bayesian hierarchical model for multiple repeated measures and survival data: an application to Parkinson's disease.

Luo S, Wang J.

Stat Med. 2014 Oct 30;33(24):4279-91. doi: 10.1002/sim.6228. Epub 2014 Jun 17.

PMID:
24935619
5.

Intent-to-treat analysis for longitudinal studies with drop-outs.

Little R, Yau L.

Biometrics. 1996 Dec;52(4):1324-33.

PMID:
8962456
6.

A Bayesian approach to joint analysis of multivariate longitudinal data and parametric accelerated failure time.

Luo S.

Stat Med. 2014 Feb 20;33(4):580-94. doi: 10.1002/sim.5956. Epub 2013 Sep 6.

7.

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.

9.

An overview of practical approaches for handling missing data in clinical trials.

DeSouza CM, Legedza AT, Sankoh AJ.

J Biopharm Stat. 2009 Nov;19(6):1055-73. doi: 10.1080/10543400903242795. Review.

PMID:
20183464
10.

Comparison of data analysis strategies for intent-to-treat analysis in pre-test-post-test designs with substantial dropout rates.

Salim A, Mackinnon A, Christensen H, Griffiths K.

Psychiatry Res. 2008 Sep 30;160(3):335-45. doi: 10.1016/j.psychres.2007.08.005. Epub 2008 Aug 20.

PMID:
18718673
11.
12.

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.

13.

Comparison of several multiple imputation strategies for repeated measures analysis of clinical scales: to truncate or not to?

Lipkovich I, Kadziola Z, Xu L, Sugihara T, Mallinckrodt CH.

J Biopharm Stat. 2014;24(4):924-43. doi: 10.1080/10543406.2014.901344.

PMID:
24697735
14.

Nonlinear multiple imputation for continuous covariate within semiparametric Cox model: application to HIV data in Senegal.

Mbougua JB, Laurent C, Ndoye I, Delaporte E, Gwet H, Molinari N.

Stat Med. 2013 Nov 20;32(26):4651-65. doi: 10.1002/sim.5854. Epub 2013 May 28.

PMID:
23712767
15.

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. 2014 Jun 6. [Epub ahead of print]

PMID:
24905056
16.

A comparison of imputation methods in a longitudinal randomized clinical trial.

Tang L, Song J, Belin TR, Un├╝tzer J.

Stat Med. 2005 Jul 30;24(14):2111-28. Erratum in: Stat Med. 2006 Mar 30;25(6):1095.

PMID:
15889392
17.

Direct likelihood analysis versus simple forms of imputation for missing data in randomized clinical trials.

Beunckens C, Molenberghs G, Kenward MG.

Clin Trials. 2005;2(5):379-86.

PMID:
16315646
18.
19.

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
20.

Missing data imputation in two phase III trials treating HIV1 infection.

Huson LW, Chung J, Salgo M.

J Biopharm Stat. 2007;17(1):159-72.

PMID:
17219761
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