Comparison of Treatments with Multiple Outcomes

  • Pascale Tubert-Bitter
  • Daniel A. Bloch
  • Tze L. Lai

Abstract

Treatment comparisons in clinical studies often involve several endpoints, particularly those related to quality of life of patients suffering from diseases like cancer and arthritis. We review traditional statistical methods for this problem and describe some new approaches. One approach is based on a new formulation of the null hypothesis that incorporates the essential univariate and multivariate features of the treatment effects. Another approach is based on assigning benefit scores to different regions of the toxicity-efficacy outcome space. A third approach involves patient thresholds for tolerating different treatments. Bootstrap methods are used to circumvent the analytic and computational complexities of the new approaches. We illustrate these approaches using data from patients with rheumatoid arthritis. In this setting, quality of life involves trade-offs between efficacy and toxicity of treatments.

Keywords

Null Hypothesis Rheumatoid Arthritis Patient Error Probability Multivariate Normal Distribution Multiple Outcome 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bloch, D.A., Lai, T.L. and Tubert-Bitter, P. (2001). One-sided tests in clinical trials with multiple endpoints. Biometrics 57, 1039–1047.PubMedCrossRefGoogle Scholar
  2. Bloch, D.A. and Silverman, B.W. (1997). Monotone discriminant functions and their applications in rheumatology. Journal of the American Statistical Association 92, 144–153.CrossRefGoogle Scholar
  3. Cook, R.J. (1996). Coupled error spending functions for parallel bivariate sequential tests. Biometrics 52, 442–450.PubMedCrossRefGoogle Scholar
  4. Cook, R.J. and Farewell, V.T. (1996). Multiplicity considerations in the design and analysis of clinical trials. Journal of the Royal Statistical Society Series A 159, 93–110.Google Scholar
  5. Efron, B. and Tibshirani, R.J. (1993). An Introduction to the Bootstrap. New York: Chapman and Hall.Google Scholar
  6. Follmann, D. (1995). Multivariate tests for multiple endpoints in clinical trials. Statistics in Medicine 14, 1163–1175.PubMedCrossRefGoogle Scholar
  7. Fries, J.F., Williams, C.A., Ramey, D.R. and Bloch, D.A. (1993). The relative toxicity of disease-modifying antirheumatic drugs. Arthritis and Rheumatism 36, 297–306.PubMedCrossRefGoogle Scholar
  8. Jennison, C. and Turnbull, B.W. (1993). Group sequential tests for bivariate response: interim analyses of clinical trials with both efficacy and safety end-points. Biometrics 49, 741–752.PubMedCrossRefGoogle Scholar
  9. Kudo, A. (1963). A multivariate analogue of the one-sided test. Biometrika50, 403–418.Google Scholar
  10. Laska, E.M., Tang, D.I. and Meisner, M.J. (1992). Testing hypotheses about an identified treatment when there are multiple endpoints. Journal of the American Statistical Association 87, 825–831.CrossRefGoogle Scholar
  11. Lin, D.Y. (1991). Nonparametric sequential testing in clinical trials with incomplete multivariate observations. Biometrika 79, 523–529.CrossRefGoogle Scholar
  12. O’Brien, P.C. (1984). Procedures for comparing samples with multiple endpoints. Biometrics 40, 1079–1087.PubMedCrossRefGoogle Scholar
  13. Perlman, M.D. (1969). One-sided testing problems in multivariate analysis. Annals of Mathematical Statistics 40, 549–567.CrossRefGoogle Scholar
  14. Perlman, M.D. and Wu, L. (1999). The emperor’s new tests (with discussion). Statistical Science 14, 355–381.Google Scholar
  15. Pocock, S.J., Geller, N.S. and Tsiatis, A.A. (1987). The analysis of multiple end-points in clinical trials. Biometrics 43, 487–498.PubMedCrossRefGoogle Scholar
  16. Ramey, D.R., Raynauld, J.P. and Fries, J.F. (1992). The health assessment questionnaire 1992: status and review. Arthritis Care Research 5, 119–129.CrossRefGoogle Scholar
  17. Su, J.Q. and Lachin, J.M., (1992). Group sequential distribution-free methods for the analysis of multivariate observations. Biometrics 48, 1033–1042.PubMedCrossRefGoogle Scholar
  18. Tang, D.I., Gnecco, C. and Geller, N.L. (1989a). An approximate likelihood ratio test for a normal mean vector with nonnegative components with applications to clinical trials. Biometrics 49, 23–30.CrossRefGoogle Scholar
  19. Tang, D.I., Gnecco, C. and Geller, N.L. (1989b). Design of group sequential clinical trials with multiple endpoints. Journal of the American Statistical Association 84, 776–779.CrossRefGoogle Scholar
  20. Tang, D.I., Geller, N.L. and Pocock, S.J. (1993). On the design and analysis of clinical trials with multiple endpoints. Biometrics 49, 23–30.PubMedCrossRefGoogle Scholar
  21. Thall, P.F. and Cheng, S.C. (1999). Treatment comparison based on two-dimensional safety and efficacy alternatives in oncology trials. Biometrics 55, 746–753.PubMedCrossRefGoogle Scholar
  22. Tubert-Bitter, P., Bloch, D.A. and Raynauld, J.P. (1995). Comparing the bivariate effects of toxicity and efficacy of treatments. Statistics in Medicine 14, 1129–1141.PubMedCrossRefGoogle Scholar
  23. Wolfe, F., Hawley, D.J. and Cathey, M.A. (1991). Clinical and health status measurements over time: prognosis and outcome assessment in rheumatoid arthritis. Journal of Rheumatology 18, 1290–1297.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2002

Authors and Affiliations

  • Pascale Tubert-Bitter
    • 1
  • Daniel A. Bloch
    • 2
  • Tze L. Lai
    • 2
  1. 1.INSERM U. 472USA
  2. 2.Stanford UniversityUSA

Personalised recommendations