A straightforward meta-analysis approach for oncology phase I dose-finding studies

TL;DR

This paper introduces a simple, robust two-stage meta-analytic method for estimating maximum tolerated doses in oncology phase I studies, accounting for heterogeneity and utilizing standard statistical tools.

Contribution

It proposes a novel two-stage approach combining penalized logistic regression and Bayesian meta-analysis for dose-finding in early-phase oncology trials.

Findings

Method performs well in Monte Carlo simulations.

Approach effectively accounts for study heterogeneity.

Illustrated with two oncology case studies.

Abstract

Phase I early-phase clinical studies aim at investigating the safety and the underlying dose-toxicity relationship of a drug or combination. While little may still be known about the compound's properties, it is crucial to consider quantitative information available from any studies that may have been conducted previously on the same drug. A meta-analytic approach has the advantages of being able to properly account for between-study heterogeneity, and it may be readily extended to prediction or shrinkage applications. Here we propose a simple and robust two-stage approach for the estimation of maximum tolerated dose(s) (MTDs) utilizing penalized logistic regression and Bayesian random-effects meta-analysis methodology. Implementation is facilitated using standard R packages. The properties of the proposed methods are investigated in Monte-Carlo simulations. The investigations are motivated and illustrated by two examples from oncology.