These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
22. 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. PubMed ID: 24430985 [TBL] [Abstract][Full Text] [Related]
23. Nonparametric analysis of dependently interval-censored failure time data. Zhu Y; Lawless JF; Cotton CA Stat Med; 2018 Sep; 37(21):3091-3105. PubMed ID: 29766531 [TBL] [Abstract][Full Text] [Related]
24. Marginal analysis of incomplete longitudinal binary data: a cautionary note on LOCF imputation. Cook RJ; Zeng L; Yi GY Biometrics; 2004 Sep; 60(3):820-8. PubMed ID: 15339307 [TBL] [Abstract][Full Text] [Related]
25. Covariate adjustment in estimating the area under ROC curve with partially missing gold standard. Liu D; Zhou XH Biometrics; 2013 Mar; 69(1):91-100. PubMed ID: 23410529 [TBL] [Abstract][Full Text] [Related]
26. Improving estimation efficiency for regression with MNAR covariates. Che M; Han P; Lawless JF Biometrics; 2020 Mar; 76(1):270-280. PubMed ID: 31393001 [TBL] [Abstract][Full Text] [Related]
27. Threshold regression to accommodate a censored covariate. Qian J; Chiou SH; Maye JE; Atem F; Johnson KA; Betensky RA Biometrics; 2018 Dec; 74(4):1261-1270. PubMed ID: 29933515 [TBL] [Abstract][Full Text] [Related]
28. Modeling event count data in the presence of informative dropout with application to bleeding and transfusion events in myelodysplastic syndrome. Diao G; Zeng D; Hu K; Ibrahim JG Stat Med; 2017 Sep; 36(22):3475-3494. PubMed ID: 28560768 [TBL] [Abstract][Full Text] [Related]
29. Correcting for dependent censoring in routine outcome monitoring data by applying the inverse probability censoring weighted estimator. Willems S; Schat A; van Noorden MS; Fiocco M Stat Methods Med Res; 2018 Feb; 27(2):323-335. PubMed ID: 26988930 [TBL] [Abstract][Full Text] [Related]
30. Nonparametric rank-based methods for group sequential monitoring of paired censored survival data. Murray S Biometrics; 2000 Dec; 56(4):984-90. PubMed ID: 11129495 [TBL] [Abstract][Full Text] [Related]
31. 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]
33. Modeling longitudinal data with nonignorable dropouts using a latent dropout class model. Roy J Biometrics; 2003 Dec; 59(4):829-36. PubMed ID: 14969461 [TBL] [Abstract][Full Text] [Related]
34. A general class of pattern mixture models for nonignorable dropout with many possible dropout times. Roy J; Daniels MJ Biometrics; 2008 Jun; 64(2):538-45. PubMed ID: 17900312 [TBL] [Abstract][Full Text] [Related]
35. A transitional model for longitudinal binary data subject to nonignorable missing data. Albert PS Biometrics; 2000 Jun; 56(2):602-8. PubMed ID: 10877323 [TBL] [Abstract][Full Text] [Related]
36. Nonparametric estimation of transition probabilities in a non-Markov illness-death model. Meira-Machado L; de Uña-Alvarez J; Cadarso-Suárez C Lifetime Data Anal; 2006 Sep; 12(3):325-44. PubMed ID: 16917736 [TBL] [Abstract][Full Text] [Related]
38. Merging multiple longitudinal studies with study-specific missing covariates: A joint estimating function approach. Wang F; Song PX; Wang L Biometrics; 2015 Dec; 71(4):929-40. PubMed ID: 26193911 [TBL] [Abstract][Full Text] [Related]
39. Cox regression model with randomly censored covariates. Atem FD; Matsouaka RA; Zimmern VE Biom J; 2019 Jul; 61(4):1020-1032. PubMed ID: 30908720 [TBL] [Abstract][Full Text] [Related]
40. Joint modeling and analysis of longitudinal data with informative observation times. Liang Y; Lu W; Ying Z Biometrics; 2009 Jun; 65(2):377-84. PubMed ID: 18759841 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]