A New Approach to Dose Estimation in Drug Development Based on Maximization of Likelihood of Grouped Data
American Journal of Theoretical and Applied Statistics
Volume 5, Issue 2-1, March 2016, Pages: 12-20
Received: Nov. 2, 2015;
Accepted: Nov. 2, 2015;
Published: Nov. 30, 2015
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Nicholas A. Nechval, Department of Mathematics, Baltic International Academy, Riga, Latvia
Gundars Berzins, Department of Management, University of Latvia, Riga, Latvia
Vadims Danovics, Department of Marketing, University of Latvia, Riga, Latvia
Identifying the ‘right’ dose is one of the most critical and difficult steps in the clinical development process of any medicinal drug. Its importance cannot be understated: selecting too high a dose can result in unacceptable toxicity and associated safety problems, while choosing too low a dose leads to smaller chances of showing sufficient efficacy in confirmatory trials, thus reducing the chance of approval for the drug. The optimal dose is the dose that gives the desired effect with minimum side effects. The dose of a drug is of course ‘optimal’ only for a given subject, but not necessarily for any other. In view of this the objective of a dose-finding trials is not to determine a single fixed dose for use in the early phases of clinical trials or in medical practice, but to determine an interval of doses within which there is a stated degree of confidence that the defined, acceptable therapeutic response and the frequency of adverse reactions will lie above and below, respectively, certain acceptable predetermined levels. If the subject samples used in the dose finding studies adequately represent the subject population for which the drug is intended, the interval of doses so defined can be applied to the subject population as a whole. In this paper, we propose the technique based on maximization of likelihood function in order to estimate the maximal tolerated dose (MTD) and minimal effective dose (MED) on the basis of l samples of subjects, which are grouped in a simplest way. The necessary and sufficient conditions for the existence and uniqueness of the maximum likelihood estimates are derived. The proposed approach to dose estimation in drug development is simple and suitable for medical practice. The numerical examples are given.
Nicholas A. Nechval,
A New Approach to Dose Estimation in Drug Development Based on Maximization of Likelihood of Grouped Data, American Journal of Theoretical and Applied Statistics. Special Issue: Novel Ideas for Efficient Optimization of Statistical Decisions and Predictive Inferences under Parametric Uncertainty of Underlying Models with Applications.
Vol. 5, No. 2-1,
2016, pp. 12-20.
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