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Items: 1 to 20 of 170

1.

A correlated random-effects model for normal longitudinal data with nonignorable missingness.

Lin H, Liu D, Zhou XH.

Stat Med. 2010 Jan 30;29(2):236-47. doi: 10.1002/sim.3760.

PMID:
19941316
2.

Adjusting for nonignorable missingness when estimating generalized additive models.

Xie H.

Biom J. 2010 Apr;52(2):186-200. doi: 10.1002/bimj.200900202.

PMID:
20422637
3.

A semiparametric 2-part mixed-effects heteroscedastic transformation model for correlated right-skewed semicontinuous data.

Lin H, Zhou XH.

Biostatistics. 2009 Oct;10(4):640-58. doi: 10.1093/biostatistics/kxp019. Epub 2009 Jun 22.

PMID:
19546166
4.
5.
6.

Estimating heterogeneity in random effects models for longitudinal data.

Lemenuel-Diot A, Mallet A, Laveille C, Bruno R.

Biom J. 2005 Jun;47(3):329-45.

PMID:
16053257
7.
8.

Impact of missing data due to drop-outs on estimators for rates of change in longitudinal studies: a simulation study.

Touloumi G, Babiker AG, Pocock SJ, Darbyshire JH.

Stat Med. 2001 Dec 30;20(24):3715-28.

PMID:
11782028
9.

Analysis of matched case-control data in presence of nonignorable missing exposure.

Sinha S, Maiti T.

Biometrics. 2008 Mar;64(1):106-14. Epub 2007 Jun 15.

PMID:
17573865
10.

Estimation and comparison of rates of change in longitudinal studies with informative drop-outs.

Touloumi G, Pocock SJ, Babiker AG, Darbyshire JH.

Stat Med. 1999 May 30;18(10):1215-33.

PMID:
10363341
11.

Covariate measurement error and the estimation of random effect parameters in a mixed model for longitudinal data.

Tosteson TD, Buonaccorsi JP, Demidenko E.

Stat Med. 1998 Sep 15;17(17):1959-71.

PMID:
9777689
12.
13.

A hierarchical model for binary data with dependence between the design and outcome success probabilities.

Todem D, Williams KP.

Stat Med. 2009 Oct 30;28(24):2967-88. doi: 10.1002/sim.3675.

PMID:
19642075
14.

Bias adjustment in analysing longitudinal data with informative missingness.

Park S, Palta M, Shao J, Shen L.

Stat Med. 2002 Jan 30;21(2):277-91.

PMID:
11782065
15.

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
16.

Multiple augmentation with partial missing regressors.

Ma S.

Biom J. 2006 Feb;48(1):83-92.

PMID:
16544814
17.

Simultaneous inference and bias analysis for longitudinal data with covariate measurement error and missing responses.

Yi GY, Liu W, Wu L.

Biometrics. 2011 Mar;67(1):67-75. doi: 10.1111/j.1541-0420.2010.01437.x.

PMID:
20528858
18.

Comparison of generalized estimating equations and quadratic inference functions using data from the National Longitudinal Survey of Children and Youth (NLSCY) database.

Odueyungbo A, Browne D, Akhtar-Danesh N, Thabane L.

BMC Med Res Methodol. 2008 May 9;8:28. doi: 10.1186/1471-2288-8-28.

19.

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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