About the event

MCP-Mod (Multiple Comparisons & Modelling) is a popular statistical methodology for model-based design and analysis of dose finding studies. This webinar will describe the theory behind MCP-Mod (plus extensions), and how to implement it within available software. Pantelis Vlachos (Cytel) will provide a brief introduction to the methodology and illustrate the MCP-MoD capabilities in EAST 6.5. Saswati Saha (Inserm, Aix-Marseille University) will discuss new variations and alternatives to MCP-Mod and show how to implement them in R. Neal Thomas (Pfizer) will present further technical details of MCP-Mod by evaluating the method using results from least squares linear model theory.

Abstracts:

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

Pantelis Vlachos - Cytel Inc. 

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®.

Understanding MCP-Mod dose finding as a method based on linear regression

Neal Thomas - Pfizer Inc.

MCP-MOD  is a testing and model selection approach utilizing contrast-based test statistics and p-values adjusted for multiple comparisons. The MCP-Mod procedure can be alternatively represented as a method based on simple linear regression, where 'simple' refers to the inclusion of an intercept and a single predictor variable, which is a transformation of dose. It is shown that the contrasts are equal to least squares linear regression slope estimates. The test for each contrast is the usual t-statistic for a null slope parameter, except that a variance estimate with fewer degrees of freedom is used in the standard error. Selecting the model corresponding to the most significant contrast p-value is equivalent to selecting the predictor variable yielding the smallest residual sum of squares. Many of the properties of MCP-Mod procedure can be understood and quantified using results from least squares linear model theory

Model based dose-finding methods in Phase II clinical trials

Saswati Saha - Inserm, Aix-Marseille University   

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

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