Bayesian Optimal Design for Phase II Screening Trials

Abstract

SUMMARYMost phase II screening designs available in the literature consider one treatment at a time. Each study is considered in isolation. We propose a more systematic decision-making approach to the phase II screening process. The sequential design allows for more efficiency and greater learning about treatments. The approach incorporates a Bayesian hierarchical model that allows combining information across several related studies in a formal way and improves estimation in small data sets by borrowing strength from other treatments. The design incorporates a utility function that includes sampling costs and possible future payoff. Computer simulations show that this method has high probability of discarding treatments with low success rates and moving treatments with high success rates to phase III trial.

Dose-Finding Methodology for Peripheral Nerve Blocks

“…In other words, Bayesian statistics employ known information, which is continuously updated and reconstructed each time new and relevant data are generated. 6 One type of Bayesian analysis specifically geared toward dose-finding methodology is the Continual Reassessment Method (CRM). 48 The latter was first pioneered in the field of toxicology where, for ethical reasons, researchers endeavored to minimize the number of patients recruited per trial.…”

“…[1][2][3][4][5][6] In anesthesia, dose-finding studies have allowed clinicians to determine the effective concentrations of inhalational agents 7 and local anesthetics (LAs) for epidural analgesia in labor. 8 Recent years have seen a resurgence of dosefinding studies in the context of peripheral nerve blocks.…”

Adaptive Design Optimization: A Mutual Information-Based Approach to Model Discrimination in Cognitive Science

“…For example, in astrophysics, ADO has been used in the design of experiments to detect extrasolar planets (Loredo, 2004). ADO has also been used in designing phase I and phase II clinical trials to ascertain the dose-response relationship of experimental drugs (Haines et al, 2003;Ding et al, 2008), as well as in estimating psychometric functions (Kujala and Lukka, 2006;Lesmes et al, 2006).…”

Advances in Statistical Approaches to Oncology Drug Development

“…In phase II development, there remains a huge unmet need for better, more efficient designs that could enable better and faster Go/No-Go decision making while controlling the cost. Some have advanced the idea of incorporating decision theory into the design of phase II studies of cancer therapies (63,64). Potential areas for research include better, faster, and cheaper surrogate endpoints for survival, treatment-selection biomarkers that predicts a large treatment effect, etc.…”