MCP-Mod in East®:  Early development dose-finding design and analysis

Selection of a dose (or doses) to carry into a confirmatory phase III study is among the most difficult decisions in drug development. A prerequisite for informed decision making and dose selection at the end of phase II is a solid characterization of the dose-response relationship(s).The MCP-Mod method combines principles of multiple comparisons with modelling techniques to provide an efficient alternative to traditional dose-finding studies which are either designed and analyzed based on multiple comparisons of active doses vs placebo within an ANOVA framework, of assume a functional relationship between response and dose according to a certain parametric model. We illustrate MCP-Mod design and analysis capabilities with East®.   

Bio: Pantelis is Director/Strategic Consultant for Cytel, Inc. based in Geneva. He joined the company in January 2013. Before that, he was a Principal Biostatistician at Merck Serono as well as a Professor of Statistics at Carnegie Mellon University  for 12 years. His research interests lie in the area of adaptive designs, mainly from a Bayesian perspective, as well as hierarchical model testing and checking although his secret passion is Text Mining. He has served as Managing Editor of the journal “Bayesian Analysis” as well as  editorial boards of several other journals and online statistical data and software archives.

Model based dose-finding methods in Phase II clinical trials

The primary objective of this presentation is to discuss dose-finding methods in Phase II clinical trials that can simultaneously establish the dose-response relationship and identify the right dose. MCP‐Mod is one of the pioneer approaches developed within the last 10 years. Though MCP-Mod is identified as an efficient statistical methodology for model-based design and analysis of Phase II dose finding studies under model uncertainty, a major disadvantage of MCP-Mod is that the parameter values of the candidate models need to be pre-specified a priori for the PoC testing step. This may lead to loss in power and unreliable model selection. Off late several new variations and alternatives to MCP-Mod are explored where the parameter values need not be pre-specified in the PoC testing step and can be estimated after the model selection step. We will briefly introduce four such state-of-art dose-finding methods, show how to implement the methods in R software and present a numerical comparison between the different new methods and the MCP-Mod approach.

Bio: Saswati completed her Ph.D as a part of IDEAS network on December 2018 from the Competence Center for Clinical Trials (KKSB) at University of Bremen under the supervision of Professor Werner Brannath. Her primary areas of research during her PhD were dose response modelling, multiple testing, drug combination studies, dose finding and confidence interval estimation for target doses in drug development.

Saswati studied at the Indian Statistical Institute, where she completed her Bachelor’s degree (2011) and Master’s degree (2013) in Statistics. After her masters she worked on credit risk modelling in two renowned financial institutions, Ernst & Young and Genpact, for two years and dealt with time series modelling for stress testing and logistic regression modelling for building scorecards.