|Grant Number:||5R01CA090998-09 Interpret this number|
|Primary Investigator:||Leblanc, Michael|
|Organization:||Fred Hutchinson Can Res Ctr|
|Project Title:||Statistical Methods for Clinical Studies|
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.
Multivariate detection of gene-gene interactions.
Authors: Rajapakse I, Perlman MD, Martin PJ, Hansen JA, Kooperberg C
Source: Genet Epidemiol, 2012 Sep;36(6), p. 622-30.
EPub date: 2012 Jul 10.
Boosting for detection of gene-environment interactions.
Authors: Pashova H, LeBlanc M, Kooperberg C
Source: Stat Med, 2013 Jan 30;32(2), p. 255-66.
EPub date: 2012 Jul 5.
Powerful cocktail methods for detecting genome-wide gene-environment interaction.
Authors: Hsu L, Jiao S, Dai JY, Hutter C, Peters U, Kooperberg C
Source: Genet Epidemiol, 2012 Apr;36(3), p. 183-94.
A novel variational Bayes multiple locus Z-statistic for genome-wide association studies with Bayesian model averaging.
Authors: Logsdon BA, Carty CL, Reiner AP, Dai JY, Kooperberg C
Source: Bioinformatics, 2012 Jul 1;28(13), p. 1738-44.
EPub date: 2012 May 4.
Choosing phase II endpoints and designs: evaluating the possibilities.
Authors: LeBlanc M, Tangen C
Source: Clin Cancer Res, 2012 Apr 15;18(8), p. 2130-2.
EPub date: 2012 Mar 8.
A strategy for full interrogation of prognostic gene expression patterns: exploring the biology of diffuse large B cell lymphoma.
Authors: Rimsza LM, Unger JM, Tome ME, Leblanc ML
Source: PLoS One, 2011;6(8), p. e22267.
EPub date: 2011 Aug 4.
Risk prediction using genome-wide association studies.
Authors: Kooperberg C, LeBlanc M, Obenchain V
Source: Genet Epidemiol, 2010 Nov;34(7), p. 643-52.
Boosting predictions of treatment success.
Authors: LeBlanc M, Kooperberg C
Source: Proc Natl Acad Sci U S A, 2010 Aug 3;107(31), p. 13559-60.
EPub date: 2010 Jul 23.
SHARE: an adaptive algorithm to select the most informative set of SNPs for candidate genetic association.
Authors: Dai JY, Leblanc M, Smith NL, Psaty B, Kooperberg C
Source: Biostatistics, 2009 Oct;10(4), p. 680-93.
EPub date: 2009 Jul 15.
Adaptively weighted association statistics.
Authors: LeBlanc M, Kooperberg C
Source: Genet Epidemiol, 2009 Jul;33(5), p. 442-52.
Interim futility analysis with intermediate endpoints.
Authors: Goldman B, LeBlanc M, Crowley J
Source: Clin Trials, 2008;5(1), p. 14-22.
Adaptive risk group refinement.
Authors: LeBlanc M, Moon J, Crowley J
Source: Biometrics, 2005 Jun;61(2), p. 370-8.
Directed indices for exploring gene expression data.
Authors: LeBlanc M, Kooperberg C, Grogan TM, Miller TP
Source: Bioinformatics, 2003 Apr 12;19(6), p. 686-93.
Partitioning and peeling for constructing prognostic groups.
Authors: LeBlanc M, Jacobson J, Crowley J
Source: Stat Methods Med Res, 2002 Jun;11(3), p. 247-74.