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A utility‐based design for randomized comparative trials with ordinal outcomes and prognostic subgroups

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  • Thomas A. Murray
  • Ying Yuan
  • Peter F. Thall
  • Joan H. Elizondo
  • Wayne L. Hofstetter

Abstract

A design is proposed for randomized comparative trials with ordinal outcomes and prognostic subgroups. The design accounts for patient heterogeneity by allowing possibly different comparative conclusions within subgroups. The comparative testing criterion is based on utilities for the levels of the ordinal outcome and a Bayesian probability model. Designs based on two alternative models that include treatment‐subgroup interactions are considered, the proportional odds model and a non‐proportional odds model with a hierarchical prior that shrinks toward the proportional odds model. A third design that assumes homogeneity and ignores possible treatment‐subgroup interactions also is considered. The three approaches are applied to construct group sequential designs for a trial of nutritional prehabilitation versus standard of care for esophageal cancer patients undergoing chemoradiation and surgery, including both untreated patients and salvage patients whose disease has recurred following previous therapy. A simulation study is presented that compares the three designs, including evaluation of within‐subgroup type I and II error probabilities under a variety of scenarios including different combinations of treatment‐subgroup interactions.

Suggested Citation

  • Thomas A. Murray & Ying Yuan & Peter F. Thall & Joan H. Elizondo & Wayne L. Hofstetter, 2018. "A utility‐based design for randomized comparative trials with ordinal outcomes and prognostic subgroups," Biometrics, The International Biometric Society, vol. 74(3), pages 1095-1103, September.
  • Handle: RePEc:bla:biomet:v:74:y:2018:i:3:p:1095-1103
    DOI: 10.1111/biom.12842
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    References listed on IDEAS

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    1. Nadine Houede & Peter F. Thall & Hoang Nguyen & Xavier Paoletti & Andrew Kramar, 2010. "Utility-Based Optimization of Combination Therapy Using Ordinal Toxicity and Efficacy in Phase I/II Trials," Biometrics, The International Biometric Society, vol. 66(2), pages 532-540, June.
    2. James P. Quirk & Rubin Saposnik, 1962. "Admissibility and Measurable Utility Functions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 29(2), pages 140-146.
    3. Beibei Guo & Ying Yuan, 2017. "Bayesian Phase I/II Biomarker-Based Dose Finding for Precision Medicine With Molecularly Targeted Agents," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 508-520, April.
    4. Bercedis Peterson & Frank E. Harrell, 1990. "Partial Proportional Odds Models for Ordinal Response Variables," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 39(2), pages 205-217, June.
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