Online Program Home
My Program

Abstract Details

Activity Number: 569 - Theory and Practice for Addressing Asymmetric Measures in Statistical Modeling
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
Sponsor: WNAR
Abstract #330177 Presentation
Title: Dependencies in Binary Regression Data Generated by Informed Sequential Dose Allocation
Author(s): Nancy Flournoy* and Assaf Oron
Companies: University of Missouri and Instutue for Disease Modeling
Keywords: Dose-response; Dose-finding; Up-and-Down Designs; Interval Designs; Continual Reassessment; Adaptive designs

In many fields such as acute toxicity & sensory studies, Phase I cancer trials, and psychometric testing, binomial regression models are used for analysis following sequential informative dose allocation. We assume the simplest general case in which a univariate binary response Y has a monotone positive response probability P(Y=1|x)=F(x) to a stimulus or treatment X; X values are sequentially selected, from a discrete set, to concentrate treatments in a region of interest under F(x). We call a positive response a toxicity and the stimulus a dose. From first principles, we describe dependencies that are introduced by sequentially choosing informative doses. We refute the prevailing notion that the number of dose-specific toxicities seen, conditional on the dose's allocation frequency, are binomial random variables; and characterize at finite sample sizes, bias of the observed toxicity rate at dose x for F(x).

This is important. Isotonic regression methods use dose-specific toxicity rates directly. Likelihood-based methods mask bias by providing first-order linear approximations to basic properties of estimators. We illustrate these findings in some common adaptive designs.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program