Grant Number:	2R01CA090998-06A2 Interpret this number
Primary Investigator:	Leblanc, Michael
Organization:	Fred Hutchinson Cancer Research Center
Project Title:	Statistical Methods for Clinical Studies
Fiscal Year:	2008

DESCRIPTION (provided by applicant): PROJECT SUMMARY/ABSTRACT Increased understanding of the genetic and biochemical mechanisms of cancer has led to new technologies for diagnosis, classification of cancers and now to the development of an array of treatments that may have efficacy for cancers with specific molecular attributes. These new treatments provide both the opportunity and necessity to develop improved designs and data adaptive analysis methods for clinical trials. Specifically, this research will consider the following: 1) Phase II and Phase III studies for new targeted treatments. Some new anticancer agents offer clinical benefits that vary with respect to target expression of the disease; therefore, better designs are needed to avoid missing promising agents. Strategies will include joint testing of subgroups and shrinkage methods. 2) Adaptive regression methods for exploring patient outcome. The complexity of results from new studies involving targeted therapy demands a better understanding of the relationships between genetic attributes and treatment efficacy. Computational methods that construct rules for patient subgroups with differing prognoses and treatment efficacy will be evaluated. 3) Longitudinal marker process data. Improved methods are also needed to understand the association of sequentially measured biomarkers and their impact and interactions with respect to treatment. We will consider causal modeling constructs to estimate effects of biomarkers in the presence of potentially time-dependant confounding on patient outcome. Software will also be implemented to facilitate the use of methods developed as part of this proposal. The evaluation of new interventions to reduce mortality and incidence of cancers is of significant public interest. Over the last few years there has been rapid progress in the development of molecular targeted therapies and in the identification of potential biomarkers. It is crucial that these new treatments and biomarkers be evaluated in a rigorous and efficient manner to best serve patients and to expand knowledge of these complex diseases. PUBLIC HEALTH RELEVANCE: The major focus of this proposal is the development of design and analysis methods appropriate for targeted agents used alone or in combination with other current cancer therapies. We will develop and evaluate the operating characteristics of flexible clinical trial designs which incorporate biologic heterogeneity based on molecular attributes. We will also study adaptive statistical algorithms for modeling patient outcome and for identifying of groups of patients who may benefit most from these new treatments.




Structured detection of interactions with the directed lasso.
Authors: Pashova H. , LeBlanc M. , Kooperberg C. .
Source: Statistics In Biosciences, 2017 Dec; 9(2), p. 676-691.
EPub date: 2016-11-29 00:00:00.0.
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Modeling The Relationship Between Progression-free Survival And Overall Survival: The Phase Ii/iii Trial
Authors: Redman M.W. , Goldman B.H. , LeBlanc M. , Schott A. , Baker L.H. .
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Significance Of Fab Subclassification Of "acute Myeloid Leukemia, Nos" In The 2008 Who Classification: Analysis Of 5848 Newly Diagnosed Patients
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Early Phase Trial Design For Assessing Several Dose Levels For Toxicity And Efficacy For Targeted Agents
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Multivariate Detection Of Gene-gene Interactions
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Design Of A Phase Iii Clinical Trial With Prospective Biomarker Validation: Swog S0819
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Cure Models As A Useful Statistical Tool For Analyzing Survival
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Seamless Phase I-ii Trial Design For Assessing Toxicity And Efficacy For Targeted Agents
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