370 related articles for article (PubMed ID: 11459446)
1. 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]
2. 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]
3. MMRM versus MI in dealing with missing data--a comparison based on 25 NDA data sets.
Siddiqui O
J Biopharm Stat; 2011 May; 21(3):423-36. PubMed ID: 21442517
[TBL] [Abstract][Full Text] [Related]
4. A bias correction in testing treatment efficacy under informative dropout in clinical trials.
Kong F; Chen YF; Jin K
J Biopharm Stat; 2009 Nov; 19(6):980-1000. PubMed ID: 20183460
[TBL] [Abstract][Full Text] [Related]
5. A comparison of the random-effects pattern mixture model with last-observation-carried-forward (LOCF) analysis in longitudinal clinical trials with dropouts.
Siddiqui O; Ali MW
J Biopharm Stat; 1998 Nov; 8(4):545-63. PubMed ID: 9855033
[TBL] [Abstract][Full Text] [Related]
6. MMRM vs. LOCF: a comprehensive comparison based on simulation study and 25 NDA datasets.
Siddiqui O; Hung HM; O'Neill R
J Biopharm Stat; 2009; 19(2):227-46. PubMed ID: 19212876
[TBL] [Abstract][Full Text] [Related]
7. Handling drop-out in longitudinal clinical trials: a comparison of the LOCF and MMRM approaches.
Lane P
Pharm Stat; 2008; 7(2):93-106. PubMed ID: 17351897
[TBL] [Abstract][Full Text] [Related]
8. The effect of correlation structure on treatment contrasts estimated from incomplete clinical trial data with likelihood-based repeated measures compared with last observation carried forward ANOVA.
Mallinckrodt CH; Kaiser CJ; Watkin JG; Molenberghs G; Carroll RJ
Clin Trials; 2004; 1(6):477-89. PubMed ID: 16279288
[TBL] [Abstract][Full Text] [Related]
9. The efficacy of duloxetine: a comprehensive summary of results from MMRM and LOCF_ANCOVA in eight clinical trials.
Mallinckrodt CH; Raskin J; Wohlreich MM; Watkin JG; Detke MJ
BMC Psychiatry; 2004 Sep; 4():26. PubMed ID: 15355546
[TBL] [Abstract][Full Text] [Related]
10. Comparisons of methods for analysis of repeated binary responses with missing data.
Frank Liu G; Zhan X
J Biopharm Stat; 2011 May; 21(3):371-92. PubMed ID: 21442514
[TBL] [Abstract][Full Text] [Related]
11. Bias reduction with an adjustment for participants' intent to dropout of a randomized controlled clinical trial.
Leon AC; Demirtas H; Hedeker D
Clin Trials; 2007; 4(5):540-7. PubMed ID: 17942469
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Last-observation-carried-forward imputation method in clinical efficacy trials: review of 352 antidepressant studies.
Woolley SB; Cardoni AA; Goethe JW
Pharmacotherapy; 2009 Dec; 29(12):1408-16. PubMed ID: 19947800
[TBL] [Abstract][Full Text] [Related]
14. Semi-parametric and non-parametric methods for clinical trials with incomplete data.
O'Brien PC; Zhang D; Bailey KR
Stat Med; 2005 Feb; 24(3):341-58. PubMed ID: 15547952
[TBL] [Abstract][Full Text] [Related]
15. Assessing response profiles from incomplete longitudinal clinical trial data under regulatory considerations.
Mallinckrodt CH; Clark SW; Carroll RJ; Molenbergh G
J Biopharm Stat; 2003 May; 13(2):179-90. PubMed ID: 12729388
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. An overview of practical approaches for handling missing data in clinical trials.
DeSouza CM; Legedza AT; Sankoh AJ
J Biopharm Stat; 2009 Nov; 19(6):1055-73. PubMed ID: 20183464
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Estimating age change in list recall in asset and health dynamics of the oldest-old: the effects of attrition bias and missing data treatment.
Kennison RF; Zelinski EM
Psychol Aging; 2005 Sep; 20(3):460-75. PubMed ID: 16248705
[TBL] [Abstract][Full Text] [Related]
20. Mixed effects logistic regression models for longitudinal binary response data with informative drop-out.
Ten Have TR; Kunselman AR; Pulkstenis EP; Landis JR
Biometrics; 1998 Mar; 54(1):367-83. PubMed ID: 9544529
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
