Assessing Response Profiles from Incomplete Longitudinal Clinical Trial Data Under Regulatory Considerations

References

• Cnaan , A. , Laird , N. M. and Slasor , P. 1997 . Using the general linear mixed model to analyze unbalanced repeated measures and longitudinal data . Stat. Med. , 16 : 2349 – 2380 .
   PubMed Web of Science ®Google Scholar
• Diggle , P. J. and Kenward , M. G. 1995 . Informative dropout in longitudinal data analysis (with discussion) . Appl. Stat. , 43 : 49 – 93 .
   Google Scholar
• Diggle , P. J. , Liang , K.-Y. and Zeger , S. L. 1994 . Analysis of Longitudinal Data Oxford, England : Clarendon Press .
   Google Scholar
• Gibbons , R. D. , Hedeker , D. , Elkin , I. , Waternaux , C. , Kraemer , H. C. , Greenhouse , J. B. , Shea , T. , Imber , S. , Stotsky , S. M. and Watkins , J. T. 1993 . Some conceptual and statistical issues in analysis of longitudinal psychiatric data . Arch. Gen. Psychiatry , 50 : 739 – 750 .
   PubMed Web of Science ®Google Scholar
• Goldstein , D. J. , Mallinckrodt , C. , Lu , Y. and Demitrack , M. A. 2002 . Duloxetine in the treatment of major depressive disorder: a double-blind clinical trial . J. Clin. Psychiatry , 63 : 225 – 231 .
   PubMed Web of Science ®Google Scholar
• in press .
   Google Scholar
• Hamilton , M. 1960 . A rating scale for depression . J. Neurol. Neurosurg. Psychiatry , 23 : 56 – 62 .
   PubMed Web of Science ®Google Scholar
• Heyting , A. , Tolboom , J. T. B. M. and Essers , J. G. A. 1992 . Statistical handling of dropouts in longitudinal clinical trials . Stat. Med. , 11 : 2043 – 2061 .
   PubMed Web of Science ®Google Scholar
• Laird , N. M. 1988 . Missing data in longitudinal studies . Stat. Med. , 7 : 305 – 315 .
   PubMed Web of Science ®Google Scholar
• Lavori , P. W. 1992 . Clinical trials in psychiatry: should protocol deviation censor patient data . Neuropsychopharmacol , 6 : 39 – 48 .
   PubMed Web of Science ®Google Scholar
• Lavori , P. W. , Dawson , R. and Shera , D. 1995 . A multiple imputation strategy for clinical trials with truncation of patient data . Stat. Med. , 14 : 1913 – 1925 .
   PubMed Web of Science ®Google Scholar
• Little , R. and Rubin , D. 1987 . Statistical Analysis with Missing Data New York : John Wiley and Sons .
   Google Scholar
• Little , R. and Yau , L. 1996 . Intent-to-treat analysis for longitudinal studies with drop-outs . Biometrics , 52 : 1324 – 1333 .
   PubMed Web of Science ®Google Scholar
• Littell , R. C. , Milliken , G. A. , Stroup , W. W. and Wolfinger , R. D. 1996 . The SAS System for Mixed Models Cary, NC : SAS Institute Inc .
   Google Scholar
• Mallinckrodt , C. H. , Clark , W. S. and David , S. R. 2001a . Accounting for dropout bias using mixed-effects models . J. Biopharm. Stat. , 11 : 9 – 21 .
   PubMedGoogle Scholar
• Mallinckrodt , C. H. , Clark , W. S. and David , S. R. 2001b . Type I error rates from mixed effects model repeated measures compared with fixed effects ANOVA with missing values imputed via LOCF . Drug Inf. J. , 35 : 1215 – 1225 .
   Web of Science ®Google Scholar
• Milliken , G. A. and Johnson , D. E. 1993 . The Analysis of Messy Data: Designed Experiments Vol. 1 , 326 New York : Chapman & Hall .
   Google Scholar
• in press .
   Google Scholar
• Rubin , D. and Shenker , N. 1991 . Multiple imputations in health care databases: an overview and some applications . Stat. Med. , 10 : 585 – 598 .
   PubMed Web of Science ®Google Scholar
• Rubin , D. B. , Stern , H. S. and Vehovar , V. 1995 . Handling “don't know” survey responses: the case of the Slovenian plebiscite . J. Am. Stat. Assoc. , 90 : 822 – 828 .
   Web of Science ®Google Scholar
• Siddiqui , O. and Ali , M. W. 1998 . A comparison of the random-effects pattern mixture model with last-observation-carried-forward (LOCF) analysis in longitudinal clinical trials with dropouts . J. Biopharm. Stat. , 8 : 545 – 563 .
   PubMedGoogle Scholar
• Verbeke , G. and Molenberghs , G. 2000 . Linear Mixed Models for Longitudinal Data New York : Springer .
   Google Scholar