A Bayesian phase I‐II design is presented that optimizes the dose of a new agent within predefined prognostic subgroups. The design is motivated by a trial to evaluate targeted agents for treating metastatic clear cell renal carcinoma, where a prognostic risk score defined by clinical variables and biomarkers is well established. Two clinical outcomes are used for dose‐finding, time‐to‐toxicity during a prespecified follow‐up period, and efficacy characterized by ordinal disease status evaluated at the end of follow‐up. A joint probability model is constructed for these outcomes as functions of dose and subgroup. The model performs adaptive clustering of adjacent subgroups having similar dose‐outcome distributions to facilitate borrowing information across subgroups. To quantify toxicity‐efficacy risk‐benefit trade‐offs that may differ between subgroups, the objective function is based on outcome utilities elicited separately for each subgroup. In the context of the renal cancer trial, a design is constructed and a simulation study is presented to evaluate the design's reliability, safety, and robustness, and to compare it to designs that either ignore subgroups or run a separate trial within each subgroup.
- Award ID(s):
- 2047631
- PAR ID:
- 10320024
- Date Published:
- Journal Name:
- Pharmaceutical Statistics
- ISSN:
- 1539-1604
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/pst.2209
