130 related articles for article (PubMed ID: 16217856)
1. An appraisal of methods for the analysis of longitudinal categorical data with MAR drop-outs.
O'Hara Hines RJ; Hines WG
Stat Med; 2005 Dec; 24(23):3549-63. PubMed ID: 16217856
[TBL] [Abstract][Full Text] [Related]
2. Performance of weighted estimating equations for longitudinal binary data with drop-outs missing at random.
Preisser JS; Lohman KK; Rathouz PJ
Stat Med; 2002 Oct; 21(20):3035-54. PubMed ID: 12369080
[TBL] [Abstract][Full Text] [Related]
3. Bias in estimating association parameters for longitudinal binary responses with drop-outs.
Fitzmaurice GM; Lipsitz SR; Molenberghs G; Ibrahim JG
Biometrics; 2001 Mar; 57(1):15-21. PubMed ID: 11252590
[TBL] [Abstract][Full Text] [Related]
4. Selection models and pattern-mixture models for incomplete data with covariates.
Michiels B; Molenberghs G; Lipsitz SR
Biometrics; 1999 Sep; 55(3):978-83. PubMed ID: 11315038
[TBL] [Abstract][Full Text] [Related]
5. Estimating equations for measures of association between repeated binary responses.
Lipsitz SR; Fitzmaurice GM
Biometrics; 1996 Sep; 52(3):903-12. PubMed ID: 8805760
[TBL] [Abstract][Full Text] [Related]
6. An application of maximum likelihood and generalized estimating equations to the analysis of ordinal data from a longitudinal study with cases missing at random.
Kenward MG; Lesaffre E; Molenberghs G
Biometrics; 1994 Dec; 50(4):945-53. PubMed ID: 7787007
[TBL] [Abstract][Full Text] [Related]
7. Joint regression and association modeling of longitudinal ordinal data.
Ekholm A; Jokinen J; McDonald JW; Smith PW
Biometrics; 2003 Dec; 59(4):795-803. PubMed ID: 14969457
[TBL] [Abstract][Full Text] [Related]
8. A bias-reduced generalized estimating equation approach for proportional odds models with small-sample longitudinal ordinal data.
Tada Y; Sato T
BMC Med Res Methodol; 2024 Jun; 24(1):140. PubMed ID: 38943068
[TBL] [Abstract][Full Text] [Related]
9. 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; 20(24):3715-28. PubMed ID: 11782028
[TBL] [Abstract][Full Text] [Related]
10. Model selection for generalized estimating equations accommodating dropout missingness.
Shen CW; Chen YH
Biometrics; 2012 Dec; 68(4):1046-54. PubMed ID: 22463099
[TBL] [Abstract][Full Text] [Related]
11. Missing data perspectives of the fluvoxamine data set: a review.
Molenberghs G; Goetghebeur EJ; Lipsitz SR; Kenward MG; Lesaffre E; Michiels B
Stat Med; 1999 Sep 15-30; 18(17-18):2449-64. PubMed ID: 10474152
[TBL] [Abstract][Full Text] [Related]
12. Generalized estimating equations for ordinal data: a note on working correlation structures.
Lumley T
Biometrics; 1996 Mar; 52(1):354-61. PubMed ID: 8934602
[TBL] [Abstract][Full Text] [Related]
13. A transition model for ordinal response data with random dropout: an application to the fluvoxamine data.
Ghahroodi ZR; Ganjali M; Berridge D
J Biopharm Stat; 2009 Jul; 19(4):658-71. PubMed ID: 20183432
[TBL] [Abstract][Full Text] [Related]
14. Efficient parameter estimation in longitudinal data analysis using a hybrid GEE method.
Leung DH; Wang YG; Zhu M
Biostatistics; 2009 Jul; 10(3):436-45. PubMed ID: 19346528
[TBL] [Abstract][Full Text] [Related]
15. GEE with Gaussian estimation of the correlations when data are incomplete.
Lipsitz SR; Molenberghs G; Fitzmaurice GM; Ibrahim J
Biometrics; 2000 Jun; 56(2):528-36. PubMed ID: 10877313
[TBL] [Abstract][Full Text] [Related]
16. Pairwise residuals and diagnostic tests for misspecified dependence structures in models for binary longitudinal data.
Breinegaard N; Rabe-Hesketh S; Skrondal A
Stat Med; 2018 Feb; 37(3):343-356. PubMed ID: 29082538
[TBL] [Abstract][Full Text] [Related]
17. The analysis of longitudinal polytomous data: generalized estimating equations and connections with weighted least squares.
Miller ME; Davis CS; Landis JR
Biometrics; 1993 Dec; 49(4):1033-44. PubMed ID: 8117899
[TBL] [Abstract][Full Text] [Related]
18. Effects of variance-function misspecification in analysis of longitudinal data.
Wang YG; Lin X
Biometrics; 2005 Jun; 61(2):413-21. PubMed ID: 16011687
[TBL] [Abstract][Full Text] [Related]
19. ORTH.Ord: An R package for analyzing correlated ordinal outcomes using alternating logistic regressions with orthogonalized residuals.
Meng C; Ryan M; Rathouz PJ; Turner EL; Preisser JS; Li F
Comput Methods Programs Biomed; 2023 Jul; 237():107567. PubMed ID: 37207384
[TBL] [Abstract][Full Text] [Related]
20. An approximate marginal logistic distribution for the analysis of longitudinal ordinal data.
Nooraee N; Abegaz F; Ormel J; Wit E; van den Heuvel ER
Biometrics; 2016 Mar; 72(1):253-61. PubMed ID: 26458164
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
