Time: 14:00 - 15:30 UK time
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\nMCP-Mod (Multiple Comparisons &\; Modelling) is a popular statistical methodology for model-
based design and analysis of dose finding studies. This webinar will descr
ibe the theory behind MCP-Mod (plus extensions)\, and how to implement it
within available software. Pantelis Vlachos (Cytel) will provide a brief i
ntroduction to the methodology and illustrate the MCP-MoD capabilities in
EAST 6.5. Saswati Saha (Inserm\, Aix-Marseille University) will discuss ne
w 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 th
eory.
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| \;\n MCP-Mod in East®\;: \;Early de velopment dose-finding design and analysis \nSe lection 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 techniqu es to provide an efficient alternative to traditional dose-finding studies which are either designed and analyzed based on multiple comparisons of a ctive doses vs placebo within an ANOVA framework\, of assume a functional relationship between response and dose according to a certain parametric m odel. We illustrate MCP-Mod design and analysis capabilities with East® \;. \; \; \n\; \nBio: \;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 Professo r of Statistics at Carnegie Mellon University \; for 12 years. His res earch interests lie in the area of adaptive designs\, mainly from a Bayesi an perspective\, as well as hierarchical model testing and checking althou gh his secret passion is Text Mining. He has served as Managing Editor of the journal &ldquo\;Bayesian Analysis&rdquo\; as well as \;editorial boards of several other journals and online statistical data and software archives. \n |
\; \n \n Neal Thomas \; \n (Pfizer Inc.) | \;\n Understanding MCP-Mod dose findi ng as a method based on linear regression \nMCP -MOD \; is a testing and model selection approach utilizing contrast-b ased test statistics and p-values adjusted for multiple comparisons. The M CP-Mod procedure can be alternatively represented as a method based on sim ple linear regression\, where 'simple' refers to the inclusion of an inter cept and a single predictor variable\, which is a transformation of dose. It is shown that the contrasts are equal to least squares linear regressio n slope estimates. The test for each contrast is the usual t-statistic for a null slope parameter\, except that a variance estimate with fewer degre es of freedom is used in the standard error. Selecting \; the model co rresponding to the most significant contrast p-value is equivalent to sele cting the predictor variable yielding the smallest residual sum of squares . Many of the properties of MCP-Mod procedure can be understood and quanti fied using results from least squares linear model theory. \nBio: \;Neal received a PhD in Statistics from th e University of Chicago. \; He is the \; leader of the Statistical Research and Innovation center at \; Pfizer working on clinical and n on-clinical applications in several therapeutic areas. Previous work exper ience includes sample surveys\, educational statistics (ETS)\, and health policy applications. \; Statistical research interests include design of observational studies\, dose response\, missing data methods\, matrix s ampling\, psychometric models\, and Bayesian statistics. \n |
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| \;\n Model based do se-finding methods in Phase II clinical trials \n < p>The primary objective of this presentation is to discuss dose-finding me thods in Phase II clinical trials that can simultaneously establish the do se-response relationship and identify the right dose. MCP‐Mod is one of th e 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 candida te 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 severa l new variations and alternatives to MCP-Mod are explored where the parame ter values need not be pre-specified in the PoC testing step and can be es timated after the model selection step. We will briefly introduce four suc h state-of-art dose-finding methods\, show how to implement the methods in R software and present a numerical comparison between the different new m ethods and the MCP-Mod approach.\nBio: \;Saswati c ompleted her Ph.D as a part of IDEAS network on December 2018 from the Com petence Center for Clinical Trials (KKSB) at University of Bremen under th e supervision of Professor Werner Brannath. Her primary areas of research during her PhD were dose response modelling\, multiple testing\, drug comb ination studies\, dose finding and confidence interval estimation for targ et doses in drug development. \nSaswati studied a t the Indian Statistical Institute\, where she completed her Bachelor&rsqu o\;s degree (2011) and Master&rsquo\;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 wi th time series modelling for stress testing and logistic regression modell ing for building scorecards. \n |
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\nThis webinar is free to attend. Please click here \;to register.
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