Stochastic Approximation and Modern Model-Based Designs for Dose-Finding Clinical Trials

Stochastic Approximation and Modern Model-based Designs for Dose-Finding Clinical Trials.

In 1951 Robbins and Monro published the seminal paper on stochastic approximation and made a specific reference to its application to the "estimation of a quantal using response, non-response data". Since the 1990s, statistical methodology for dose-finding studies has grown into an active area of research. The dose-finding problem is at its core a percentile estimation problem and is in line wi...

Dose-finding Designs for Phase Ii Clinical Trials

Adaptive designs for phase II cross-over dose-finding trials using Bayesian model averaging

Finding the right dose of a novel treatment is one of the most important tasks in early drug development. However, there is often uncertainty about the form of the relationship between dose and patient response at the time that the dose-finding trials are designed. In this presentation we develop Bayesian adaptive designs for Phase II cross-over trials conducted to estimate the minimum effectiv...

Practical considerations for optimal designs in clinical dose finding studies.

A key objective in the clinical development of a medicinal drug is the determination of an adequate dose level and, more broadly, the characterization of its dose response relationship. If the dose is set too high, safety and tolerability problems are likely to result, while selecting too low a dose makes it difficult to establish adequate efficacy in the confirmatory phase, possibly leading to...

Optimal designs for dose finding studies

Identifying the “right” dose is one of the most critical and difficult steps in the clinical development process of any medicinal drug. Its importance cannot be understated: selecting too high a dose can result in unacceptable toxicity and associated safety problems, while choosing too low a dose leads to smaller chances of showing sufficient efficacy in confirmatory trials, thus reducing the c...