Functional Data Models for Dose Finding Studies

Oregon Convention Center 

The primary aim of dose-finding studies is to pinpoint the optimal dose level based on subjects' responses, focusing on 'Efficacy' and 'Toxicity.' The optimal dose is identified at the point of maximum probability, where efficacy is significant without toxicity. While some studies use Emax, quadratic, or non-linear models, they are unsuitable for non-monotonic curves. Cripper & Orsini (2016) proposed regression splines, but they may not sufficiently describe reasonable dose-response distributions. This paper introduces functional data models for dose-finding studies, presenting a novel approach by applying them to meta-analysis data. We focus on three outcome probabilities: P(Efficacy), P(Toxicity), and P(Efficacy but No Toxicity), guided by monotonic and unimodal assumptions. Our functional data models estimate these probability distributions and introduce adjusted confidence intervals. Finally, we apply these models to analyze data on alcohol consumption and colorectal cancer.

Functional Data

Dose Study

Meta-Analysis Data

Efficacy and Toxicity

Functional Anova

Smoothing methods 

Section on Nonparametric Statistics