A utility approach to individualized optimal dose selection using biomarkers.

A utility approach to individualized optimal dose selection using biomarkers.

Publication date: Nov 06, 2019

In many settings, including oncology, increasing the dose of treatment results in both increased efficacy and toxicity. With the increasing availability of validated biomarkers and prediction models, there is the potential for individualized dosing based on patient specific factors. We consider the setting where there is an existing dataset of patients treated with heterogenous doses and including binary efficacy and toxicity outcomes and patient factors such as clinical features and biomarkers. The goal is to analyze the data to estimate an optimal dose for each (future) patient based on their clinical features and biomarkers. We propose an optimal individualized dose finding rule by maximizing utility functions for individual patients while limiting the rate of toxicity. The utility is defined as a weighted combination of efficacy and toxicity probabilities. This approach maximizes overall efficacy at a prespecified constraint on overall toxicity. We model the binary efficacy and toxicity outcomes using logistic regression with dose, biomarkers and dose-biomarker interactions. To incorporate the large number of potential parameters, we use the LASSO method. We additionally constrain the dose effect to be non-negative for both efficacy and toxicity for all patients. Simulation studies show that the utility approach combined with any of the modeling methods can improve efficacy without increasing toxicity relative to fixed dosing. The proposed methods are illustrated using a dataset of patients with lung cancer treated with radiation therapy.

Li, P., Taylor, J.M.G., Kong, S., Jolly, S., and Schipper, M.J. A utility approach to individualized optimal dose selection using biomarkers. 05723. 2019 Biom J.

Concepts Keywords
Binary Medicine
Biomarker Clinical medicine
Biomarkers Branches of biology
LASSO Method Toxicology
Logistic Regression Biomarkers
Lung Biotechnology
Oncology Chemical pathology
Toxicity Dose
Toxicity
7+3
Simulation

Semantics

Type Source Name
gene UNIPROT LARGE1
disease MESH lung cancer
disease DOID lung cancer
gene UNIPROT TNFRSF19

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