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Items: 1 to 20 of 103

1.

Subjective prior distributions for modeling longitudinal continuous outcomes with non-ignorable dropout.

Paddock SM, Ebener P.

Stat Med. 2009 Feb 15;28(4):659-78. doi: 10.1002/sim.3484.

2.

Pattern mixture models and latent class models for the analysis of multivariate longitudinal data with informative dropouts.

Dantan E, Proust-Lima C, Letenneur L, Jacqmin-Gadda H.

Int J Biostat. 2008;4(1):Article 14.

PMID:
22462120
3.

Bayesian latent-class mixed-effect hybrid models for dyadic longitudinal data with non-ignorable dropouts.

Ahn J, Liu S, Wang W, Yuan Y.

Biometrics. 2013 Dec;69(4):914-24. doi: 10.1111/biom.12100. Epub 2013 Nov 6.

4.
5.

An index of local sensitivity to non-ignorability for multivariate longitudinal mixed data with potential non-random dropout.

Mahabadi SE, Ganjali M.

Stat Med. 2010 Jul 30;29(17):1779-92. doi: 10.1002/sim.3948.

PMID:
20658547
6.

Eliciting and using expert opinions about dropout bias in randomized controlled trials.

White IR, Carpenter J, Evans S, Schroter S.

Clin Trials. 2007;4(2):125-39.

PMID:
17456512
7.

Bayesian informative dropout model for longitudinal binary data with random effects using conditional and joint modeling approaches.

Chan JS.

Biom J. 2016 May;58(3):549-69. doi: 10.1002/bimj.201400064. Epub 2015 Oct 15.

PMID:
26467236
8.
9.

An exploration of fixed and random effects selection for longitudinal binary outcomes in the presence of nonignorable dropout.

Li N, Daniels MJ, Li G, Elashoff RM.

Biom J. 2013 Jan;55(1):17-37. doi: 10.1002/bimj.201100107. Epub 2012 Nov 2.

10.

A Bayesian model for longitudinal count data with non-ignorable dropout.

Kaciroti NA, Raghunathan TE, Schork MA, Clark NM.

J R Stat Soc Ser C Appl Stat. 2008 Dec 1;57(5):521-534.

11.
12.

Marginalized transition shared random effects models for longitudinal binary data with nonignorable dropout.

Lee M, Lee K, Lee J.

Biom J. 2014 Mar;56(2):230-42. doi: 10.1002/bimj.201200085. Epub 2014 Jan 15.

PMID:
24430985
13.
14.

A simple imputation method for longitudinal studies with non-ignorable non-responses.

Wang M, Fitzmaurice GM.

Biom J. 2006 Apr;48(2):302-18.

PMID:
16708780
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18.

Pseudo-likelihood methods for longitudinal binary data with non-ignorable missing responses and covariates.

Parzen M, Lipsitz SR, Fitzmaurice GM, Ibrahim JG, Troxel A.

Stat Med. 2006 Aug 30;25(16):2784-96.

PMID:
16345018
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