BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

413 related articles for article (PubMed ID: 23193075)

  • 1. A structured framework for assessing sensitivity to missing data assumptions in longitudinal clinical trials.
    Mallinckrodt CH; Lin Q; Molenberghs M
    Pharm Stat; 2013; 12(1):1-6. PubMed ID: 23193075
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A structured approach to choosing estimands and estimators in longitudinal clinical trials.
    Mallinckrodt CH; Lin Q; Lipkovich I; Molenberghs G
    Pharm Stat; 2012; 11(6):456-61. PubMed ID: 22962024
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A local influence sensitivity analysis for incomplete longitudinal depression data.
    Shen S; Beunckens C; Mallinckrodt C; Molenberghs G
    J Biopharm Stat; 2006 May; 16(3):365-84. PubMed ID: 16724491
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Analyzing incomplete longitudinal clinical trial data.
    Molenberghs G; Thijs H; Jansen I; Beunckens C; Kenward MG; Mallinckrodt C; Carroll RJ
    Biostatistics; 2004 Jul; 5(3):445-64. PubMed ID: 15208205
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Handling of missing data in long-term clinical trials: a case study.
    Janssens M; Molenberghs G; Kerstens R
    Pharm Stat; 2012; 11(6):442-8. PubMed ID: 22888095
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Comparison of data analysis strategies for intent-to-treat analysis in pre-test-post-test designs with substantial dropout rates.
    Salim A; Mackinnon A; Christensen H; Griffiths K
    Psychiatry Res; 2008 Sep; 160(3):335-45. PubMed ID: 18718673
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Comparison of imputation and modelling methods in the analysis of a physical activity trial with missing outcomes.
    Wood AM; White IR; Hillsdon M; Carpenter J
    Int J Epidemiol; 2005 Feb; 34(1):89-99. PubMed ID: 15333619
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Analysis of longitudinal trials with protocol deviation: a framework for relevant, accessible assumptions, and inference via multiple imputation.
    Carpenter JR; Roger JH; Kenward MG
    J Biopharm Stat; 2013; 23(6):1352-71. PubMed ID: 24138436
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Intent-to-treat analysis for longitudinal studies with drop-outs.
    Little R; Yau L
    Biometrics; 1996 Dec; 52(4):1324-33. PubMed ID: 8962456
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A multiple-imputation-based approach to sensitivity analyses and effectiveness assessments in longitudinal clinical trials.
    Ayele BT; Lipkovich I; Molenberghs G; Mallinckrodt CH
    J Biopharm Stat; 2014; 24(2):211-28. PubMed ID: 24605966
    [TBL] [Abstract][Full Text] [Related]  

  • 11. An analytic method for the placebo-based pattern-mixture model.
    Lu K
    Stat Med; 2014 Mar; 33(7):1134-45. PubMed ID: 24122822
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Evaluation of overall treatment effect in MMRM.
    Song T; Dong Q; Sankoh AJ; Molenberghs G
    J Biopharm Stat; 2013; 23(6):1281-93. PubMed ID: 24138432
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Assessing missing data assumptions in longitudinal studies: an example using a smoking cessation trial.
    Yang X; Shoptaw S
    Drug Alcohol Depend; 2005 Mar; 77(3):213-25. PubMed ID: 15734221
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Estimating the effect of multiple imputation on incomplete longitudinal data with application to a randomized clinical study.
    Fong DY; Rai SN; Lam KS
    J Biopharm Stat; 2013; 23(5):1004-22. PubMed ID: 23957512
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Assessment of type I error rate associated with dose-group switching in a longitudinal Alzheimer trial.
    Habteab Ghebretinsae A; Molenberghs G; Dmitrienko A; Offen W; Sethuraman G
    J Biopharm Stat; 2014; 24(3):660-84. PubMed ID: 24697817
    [TBL] [Abstract][Full Text] [Related]  

  • 16. An alternative way to classify missing data mechanism in clinical trials--a dialogue on missing data.
    Wei L
    J Biopharm Stat; 2011 Mar; 21(2):355-61. PubMed ID: 21391007
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A latent-class mixture model for incomplete longitudinal Gaussian data.
    Beunckens C; Molenberghs G; Verbeke G; Mallinckrodt C
    Biometrics; 2008 Mar; 64(1):96-105. PubMed ID: 17608789
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Accounting for dropout bias using mixed-effects models.
    Mallinckrodt CH; Clark WS; David SR
    J Biopharm Stat; 2001; 11(1-2):9-21. PubMed ID: 11459446
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Sensitivity analysis of intention-to-treat estimates when withdrawals are related to unobserved compliance status.
    Salim A; Mackinnon A; Griffiths K
    Stat Med; 2008 Apr; 27(8):1164-79. PubMed ID: 17724782
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Treatment effects in randomized longitudinal trials with different types of nonignorable dropout.
    Yang M; Maxwell SE
    Psychol Methods; 2014 Jun; 19(2):188-210. PubMed ID: 24079928
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 21.