A system for determining maximum tolerated dose in clinical trial

Abstract

Toxicity study, especially in determining the maximum tolerated dose (MTD) in phase I clinical trial, is an important step in developing new life-saving drugs. In practice, toxicity levels may be categorised as binary grades, multiple grades, or in a more generalised case, continuous grades. In this study, we propose an overall MTD framework that includes all the aforementioned cases for a single toxicity outcome (response). The mechanism of determining MTD involves a function that is predetermined by user. Analytic properties of such a system are investigated and simulation studies are performed for various scenarios. The concept of the continual reassessment method (CRM) is also implied in the framework and Bayesian analysis, including Markov chain Monte Carlo (MCMC) methods are used in estimating the model parameters.

Keywords:

• Bayesian methods
• continual reassessment method (CRM)
• drug dose finding
• Markov chain Monte Carlo (MCMC) method
• maximum tolerated dose (MTD)
• phase I clinical trial
• toxicity responses

Acknowledgements

Research of Keying Ye and Min Wang were partially supported by grants from the College of Business at the University of Texas at San Antonio.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Keying Ye

Keying Ye is a professor of Statistics in the Department of Management Science and Statistics at the University of Texas at San Antonio. His research interests include Bayesian analysis, statistical inference and decision theory, and statistical applications.

Xiaobin Yang

Xiaobin Yang is a senior statistician in the HouseCanary Inc., San Antonio, Texas. His research interests include statistical analysis and applications.

Ying Ji

Ying Ji is a statistical consultant and her research interests include statistical analysis and applications.

Min Wang

Min Wang is an associate professor of Statistics in the Department of Management Science and Statistics at the University of Texas at San Antonio. His research interests include Bayesian statistics, high dimensional inference and statistical applications and machine learning.