Analysis of Longitudinal Quality of Life Data with Informative Dropout

  • Margaret C. Wu
  • Craig B. Borkowf
  • Paul S. Albert


An important objective of many clinical trials is the estimation and comparison of longitudinal changes in quality-of-life measurements. One difficulty encountered in these trials is that participants may drop out of the study due to worsening disease and miss follow-up evaluations after the time of dropout. When the dropout process depends on the true quality-of-life measurement, it is called informative dropout. If the analysis of the data ignores information about the informative dropout process, the estimates of changes in quality-of-life measurements may be biased. In this chapter we review several approaches for adjusting for informative dropout. We consider the situations where the probability of dropout depends on only the last observed measurement prior to dropout, on only the unobserved measurement at the time of dropout, and on both of these measurements. We illustrate these concepts with an analysis of a clinical trial for the treatment of asthma and provide examples of emphysema and prostate cancer treatment trials in which these approaches are also applicable.


Mean Square Error Lung Volume Reduction Surgery Observe Response Simulated Clinical Trial Asthma Symptom Score 
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  1. 1.
    National Emphysema Treatment Trial (NETT) Research Group (1999). Rationale and design of the National Emphysema Treatment Trial: a prospective randomized trial of lung volume reduction surgery. Chest 116, 1750–1761.CrossRefGoogle Scholar
  2. 2.
    Childhood Asthma Management Program (CAMP) Research Group (1999). The childhood asthma management program (CAMP): design, rationale, and methods. Controlled Clinical Trials 20, 91–120.CrossRefGoogle Scholar
  3. 3.
    Moinpour, C.M., Savage, M.J., Troxel, A., Lovato, L.C., Eisenberger, M., Veith, R.W., Higgins, B., Skeel, R., Yee, M., Blumenstein, B.A., Crawford, E.D. and Meyskens, F.L. (1998). Quality of life in advanced prostate cancer: results of a randomized therapeutic trial. Journal of the National Cancer Institute 90, 1537–1544.PubMedCrossRefGoogle Scholar
  4. 4.
    Laird, N.M. and Ware, J.H. (1982). Random-effects models for longitudinal data. Biometrics 38, 963–974.PubMedCrossRefGoogle Scholar
  5. 5.
    Breslow, N.E. and Clayton, D.G. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 88, 9–25.Google Scholar
  6. 6.
    Follmann, D. and Wu, M. (1995). An approximate generalized linear-model with random effects for informative missing data. Biometrics 51, 151–168.PubMedCrossRefGoogle Scholar
  7. 7.
    Wu, M.C. and Bailey, K. R. (1989). Estimation and comparison of changes in the presence of informative right censoring: conditional linear models. Biometrics 45, 939–955.PubMedCrossRefGoogle Scholar
  8. 8.
    Little, R.A. and Rubin, D.B. (1987). Statistical Analysis with Missing Data. New York: John Wiley and Sons.Google Scholar
  9. 9.
    Diggle, P.J., Liang, K.Y. and Zeger, S.L. (1994). Analysis of Longitudinal Data. Oxford: Oxford University Press.Google Scholar
  10. 10.
    Wu, M.C. and Carroll, R.J. (1988). Estimation and comparison of changes in the presence of informative right censoring by modeling the censoring process. Biometrics 44, 175–188.CrossRefGoogle Scholar
  11. 11.
    Mori, M., Woodworth, G.G. and Woolson, R.F. (1992). Application of empirical Bayes inference to estimation of rate of change in the presence of informative right censoring. Statistics in Medicine 11, 621–631.PubMedCrossRefGoogle Scholar
  12. 12.
    Mori, M., Woolson, R.F. and Woodworth, G.G. (1994). Slope estimation in the presence of informative right censoring: modeling the number of observations as a geometric random variable. Biometrics 50, 39–50.PubMedCrossRefGoogle Scholar
  13. 13.
    Ten Have, T.R., Kunselman, A.R., Pulstenis, E.P. and Landis, J.R. (1998). Mixed effects logistic regression models for longitudinal binary response data with informative drop-out. Biometrics 54, 367–383.PubMedCrossRefGoogle Scholar
  14. 14.
    Albert, P.S. and Follmann, D.A. (2000). Modeling repeated count data subject to informative dropout. Biometrics 56, 667–677.PubMedCrossRefGoogle Scholar
  15. 15.
    SAS Institute, Inc. (1990). SAS/STAT User’s Guide, Version 6, 4th Edition. Cary, NC: SAS Institute.Google Scholar
  16. 16.
    Lindstrom, M.J. and Bates, D.M. (1990). Nonlinear mixed effects models for repeated measures data. Biometrics 46, 673–687.PubMedCrossRefGoogle Scholar
  17. 17.
    Wu, M.C. and Follmann, D.A. (1999). Use of summary measures to adjust for informative missingness in repeated measures data with random effects. Biometrics 55, 75–84.PubMedCrossRefGoogle Scholar
  18. 18.
    Wang-Clow, F., Lange, M., Laird, N.M. and Ware, J.H. (1995). A simulation study of estimators for rates of change in longitudinal studies with attrition. Statistics in Medicine 14, 283–297.PubMedCrossRefGoogle Scholar
  19. 19.
    Wu, M.C., Albert, P.S. and Wu, B.U. (2001). Adjusting for dropout in clinical trials with repeated measures: design and analysis issues. Statistics in Medicine, 20, 93–108.PubMedCrossRefGoogle Scholar
  20. 20.
    Goldberger, A.S. (1983). Abnormal selection bias. In: Karlin, S., Amemiya, T. and Goodman, L.A. (eds.). Studies in Econometrics, Time Series, and Multivariate Statistics. New York: Academic Press, Inc.Google Scholar
  21. 21.
    Olsen, R.J. (1980). A least-squares correction for selection bias. Econometrica 48, 1815–1820.CrossRefGoogle Scholar
  22. 22.
    Hogan, W. and Laird, N.M. (1997). Mixture models for the joint distribution of repeated measures and event times. Statistics in Medicine 16, 239–257.PubMedCrossRefGoogle Scholar
  23. 23.
    Little, R.J.A. (1993). Pattern mixture models for multivariate incomplete data. Journal of the American Statistical Association 88, 125–134.Google Scholar
  24. 24.
    Childhood Asthma Management Program (CAMP) Research Group (2000). Long-term effects of budesonide or nedocromil in children with asthma. New England Journal of Medicine 343, 1054–1063.CrossRefGoogle Scholar
  25. 25.
    Zucker, D.M., Zerbe, G.O. and Wu, M.C. (1995). Inference for the association between coefficients in a multivariate growth curve model. Biometrics 51, 413–424.PubMedCrossRefGoogle Scholar
  26. 26.
    Wu, M.C., Hunsberger, S. and Zucker, D. (1994). Testing for differences in changes in the presence of informative censoring: parametric and non-parametric methods. Statistics in Medicine 13, 635–646.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2002

Authors and Affiliations

  • Margaret C. Wu
    • 1
  • Craig B. Borkowf
    • 2
  • Paul S. Albert
    • 2
  1. 1.National Heart, Lung, and Blood InstituteUSA
  2. 2.National Cancer InstituteUSA

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