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Analysis of clinical trials with biologics using dose–time‐response models
Author(s) -
Lange Markus R.,
Schmidli Heinz
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6551
Subject(s) - medicine , clinical trial , bayesian probability , inference , dosing , drug development , regimen , computer science , drug , pharmacology , artificial intelligence
Biologics such as monoclonal antibodies are increasingly and successfully used for the treatment of many chronic diseases. Unlike conventional small drug molecules, which are commonly given as tablets once daily, biologics are typically injected at much longer time intervals, that is, weeks or months. Hence, both the dose and the time interval have to be optimized during the drug development process for biologics. To identify an adequate regimen for the investigated biologic, the dose–time‐response relationship must be well characterized, based on clinical trial data. The proposed approach uses semi‐mechanistic nonlinear regression models to describe and predict the time‐changing response for complex dosing regimens. Both likelihood‐based and Bayesian methods for inference and prediction are discussed. The methodology is illustrated with data from a clinical study in an auto‐immune disease. Copyright © 2015 John Wiley & Sons, Ltd.