Speaker
Description
In oncology drug development, phase II dose-finding studies are essential to identify the most promising dose levels for confirmatory phase III trials. Traditionally, dose selection is based on the maximum tolerated dose, which does not necessarily correspond to the optimal dose in terms of efficacy and safety. To address the challenge of dose optimization, the Oncology Center of Excellence of the U.S. Food and Drug Administration launched the OPTIMUS project. One challenge that arises in this context is the accurate estimation of therapeutic effects. In practice, the true treatment effect is often overestimated in phase II studies, and results cannot always be confirmed in subsequent phase III trials. While computational bias-correction methods for dose-finding trials have been presented for normally distributed outcomes (e.g., bootstrap-based methods, [1]), none have so far been presented for binary endpoints in this context, which are common in oncological trials (e.g., tumor response or occurrence of toxicities).
Objective:
This work aims to evaluate statistical methods for reducing selection bias in dose-finding studies with binary outcomes. Specifically, the performance of bootstrap-based methods, adapted from approaches presented for normally distributed endpoints, as well as additional Bayesian hierarchical approaches is investigated through simulation studies focusing on tumor response as the binary endpoint.
Methods:
A simulation study is conducted considering different underlying dose-response relationships (e.g. Emax, linear, logistic, etc.).
First, single and double (non-)parametric bootstrap methods, originally proposed for normally distributed endpoints, are evaluated. The impact of different numbers of bootstrap repetitions on the bias and mean squared error (MSE) of the true maximal dose is compared across methods and dose–response relationships.
For Bayesian hierarchical models, various hyperpriors for the variance parameter in a Bayesian hierarchical model are investigated, including Gamma, Half-t, and Uniform distributions with different parameterizations. The final model will be based on the hyperprior that minimizes bias and MSE of the estimated maximum response.
Based on these results, bootstrap and Bayesian methods will be jointly evaluated and compared with other approaches, such as additive und multiplicate shrinkage methods.
Outlook:
The simulation framework enables a systematic comparison of bias-reduction methods for binary endpoints under a range of dose-response patterns. Results will be presented and may provide guidance on selecting appropriate methods to minimize bias in dose selection.
[1] Zhan T. A class of computational methods to reduce selection bias when designing
Phase 3 clinical trials. Statistics in Medicine. 2024;43(10):1993-2006. doi: 10.1002/sim.10041