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.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

127 related articles for article (PubMed ID: 37094843)

  • 21. Identifying the types of missingness in quality of life data from clinical trials.
    Curran D; Bacchi M; Schmitz SF; Molenberghs G; Sylvester RJ
    Stat Med; 1998 Mar 15-Apr 15; 17(5-7):739-56. PubMed ID: 9549820
    [TBL] [Abstract][Full Text] [Related]  

  • 22. 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]  

  • 23. Ignoring Non-ignorable Missingness.
    Rabe-Hesketh S; Skrondal A
    Psychometrika; 2023 Mar; 88(1):31-50. PubMed ID: 36539650
    [TBL] [Abstract][Full Text] [Related]  

  • 24. A hidden Markov model for continuous longitudinal data with missing responses and dropout.
    Pandolfi S; Bartolucci F; Pennoni F
    Biom J; 2023 Jun; 65(5):e2200016. PubMed ID: 37035989
    [TBL] [Abstract][Full Text] [Related]  

  • 25. 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]  

  • 26. Effect of heteroscedasticity between treatment groups on mixed-effects models for repeated measures.
    Gosho M; Maruo K
    Pharm Stat; 2018 Sep; 17(5):578-592. PubMed ID: 29978944
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Misspecification of the covariance structure in generalized linear mixed models.
    Chavance M; Escolano S
    Stat Methods Med Res; 2016 Apr; 25(2):630-43. PubMed ID: 23070599
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Linear Increments with Non-monotone Missing Data and Measurement Error.
    Seaman SR; Farewell D; White IR
    Scand Stat Theory Appl; 2016 Dec; 43(4):996-1018. PubMed ID: 27867251
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Diagnosing and Handling Common Violations of Missing at Random.
    Ji F; Rabe-Hesketh S; Skrondal A
    Psychometrika; 2023 Dec; 88(4):1123-1143. PubMed ID: 36600171
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Analysis of crossover designs for longitudinal binary data with ignorable and nonignorable dropout.
    Wang X; Chinchilli VM
    Stat Methods Med Res; 2022 Jan; 31(1):119-138. PubMed ID: 34779672
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Asymptotic bias of normal-distribution-based maximum likelihood estimates of moderation effects with data missing at random.
    Zhang Q; Yuan KH; Wang L
    Br J Math Stat Psychol; 2019 May; 72(2):334-354. PubMed ID: 30474256
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Score test for missing at random or not under logistic missingness models.
    Wang H; Lu Z; Liu Y
    Biometrics; 2023 Jun; 79(2):1268-1279. PubMed ID: 35348206
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Application of pattern mixture models to address missing data in longitudinal data analysis using SPSS.
    Son H; Friedmann E; Thomas SA
    Nurs Res; 2012; 61(3):195-203. PubMed ID: 22551994
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Information matrix estimation procedures for cognitive diagnostic models.
    Liu Y; Xin T; Andersson B; Tian W
    Br J Math Stat Psychol; 2019 Feb; 72(1):18-37. PubMed ID: 29508383
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Analytical results in longitudinal studies depended on target of inference and assumed mechanism of attrition.
    Jones M; Mishra GD; Dobson A
    J Clin Epidemiol; 2015 Oct; 68(10):1165-75. PubMed ID: 25920943
    [TBL] [Abstract][Full Text] [Related]  

  • 36. 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. PubMed ID: 22462120
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Pay Attention to the Ignorable Missing Data Mechanisms! An Exploration of Their Impact on the Efficiency of Regression Coefficients.
    Chen L; Savalei V; Rhemtulla M
    Multivariate Behav Res; 2023; 58(6):1134-1159. PubMed ID: 37039444
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Bias in longitudinal data analysis with missing data using typical linear mixed-effects modelling and pattern-mixture approach: an analytical illustration.
    Yang M; Wang L; Maxwell SE
    Br J Math Stat Psychol; 2015 May; 68(2):246-67. PubMed ID: 25098455
    [TBL] [Abstract][Full Text] [Related]  

  • 39. A Bayesian Latent Variable Selection Model for Nonignorable Missingness.
    Du H; Enders C; Keller BT; Bradbury TN; Karney BR
    Multivariate Behav Res; 2022; 57(2-3):478-512. PubMed ID: 33529056
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Empirical-likelihood-based criteria for model selection on marginal analysis of longitudinal data with dropout missingness.
    Chen C; Shen B; Zhang L; Xue Y; Wang M
    Biometrics; 2019 Sep; 75(3):950-965. PubMed ID: 31004449
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 7.