|Grant Number:||5P01CA053996-35 Interpret this number|
|Primary Investigator:||Prentice, Ross|
|Organization:||Fred Hutchinson Cancer Research Center|
|Project Title:||Statistical Methods for Medical Studies|
DESCRIPTION (provided by applicant): This renewal application proposes to carry out a Program of statistical methods research to address gaps and barriers arising in three biomedical research sectors: Project 1, 'Chronic Disease Population Science Research Issues and Strategies', aims to develop methods that are mostly pertinent to the prevention of cancer and other chronic diseases. These include methods for the analysis of multivariate failure time and longitudinal data, and for disease risks attribution; methods for correcting dietary and physical activity assessment data using biomarkers, and for new biomarker development; methods for using high dimensional genotype data to identify the preferred treatment or intervention for individuals; and methods for biological network development and for preventive intervention development. Project 2, 'Genetic Epidemiology Methods', focuses primarily on methods needed to more fully understand the genetic contribution to disease risk in the post-genome wide association study era. These include methods for identifying combinations of environmental factors that modify genetic effects; methods for rare variant association studies; methods for penetrance function estimation; and methods for using genotype data to facilitate environmental factor association studies. Project 3, 'Use of Biomarkers in Diagnosis, Prognosis, Risk Prediction and Early Detection of Disease', proposes to develop novel study designs for prognostic biomarker evaluation to improve inference on ROC curves through the use of standardized biomarker values; and to develop group sequential design procedures for biomarker evaluation. Collectively, these projects will apply the talents of 15 active biostatistical methodologists, in an interactive and coordinated manner, to address statistical issues that are among the most important for progress in chronic disease population research.
Simultaneous association of total energy consumption and activity-related energy expenditure with risks of cardiovascular disease, cancer, and diabetes among postmenopausal women.
Authors: Zheng C, Beresford SA, Van Horn L, Tinker LF, Thomson CA, Neuhouser ML, Di C, Manson JE, Mossavar-Rahmani Y, Seguin R, Manini T, LaCroix AZ, Prentice RL
Source: Am J Epidemiol, 2014 Sep 1;180(5), p. 526-35.
EPub date: 2014 Jul 12.
Robust estimation for secondary trait association in case-control genetic studies.
Authors: Tapsoba Jde D, Kooperberg C, Reiner A, Wang CY, Dai JY
Source: Am J Epidemiol, 2014 May 15;179(10), p. 1264-72.
EPub date: 2014 Apr 9.
Biomarker-calibrated protein intake and bone health in the Women's Health Initiative clinical trials and observational study.
Authors: Beasley JM, LaCroix AZ, Larson JC, Huang Y, Neuhouser ML, Tinker LF, Jackson R, Snetselaar L, Johnson KC, Eaton CB, Prentice RL
Source: Am J Clin Nutr, 2014 Apr;99(4), p. 934-40.
EPub date: 2014 Feb 19.
Sedentary behavior and mortality in older women: the Women's Health Initiative.
Authors: Seguin R, Buchner DM, Liu J, Allison M, Manini T, Wang CY, Manson JE, Messina CR, Patel MJ, Moreland L, Stefanick ML, Lacroix AZ
Source: Am J Prev Med, 2014 Feb;46(2), p. 122-35.
Measurement error corrected sodium and potassium intake estimation using 24-hour urinary excretion.
Authors: Huang Y, Van Horn L, Tinker LF, Neuhouser ML, Carbone L, Mossavar-Rahmani Y, Thomas F, Prentice RL
Source: Hypertension, 2014 Feb;63(2), p. 238-44.
EPub date: 2013 Nov 25.
Modifying effect of obesity on the association between sitting and incident diabetes in post-menopausal women.
Authors: Manini TM, Lamonte MJ, Seguin RA, Manson JE, Hingle M, Garcia L, Stefanick ML, Rodriguez B, Sims S, Song Y, Limacher M
Source: Obesity (Silver Spring), 2014 Apr;22(4), p. 1133-41.
EPub date: 2013 Dec 4.
An exploratory study of respiratory quotient calibration and association with postmenopausal breast cancer.
Authors: Prentice RL, Neuhouser ML, Tinker LF, Pettinger M, Thomson CA, Mossavar-Rahmani Y, Thomas F, Qi L, Huang Y
Source: Cancer Epidemiol Biomarkers Prev, 2013 Dec;22(12), p. 2374-83.
EPub date: 2013 Oct 9.
Associations of serum insulin-like growth factor-I and insulin-like growth factor-binding protein 3 levels with biomarker-calibrated protein, dairy product and milk intake in the Women's Health Initiative.
Authors: Beasley JM, Gunter MJ, LaCroix AZ, Prentice RL, Neuhouser ML, Tinker LF, Vitolins MZ, Strickler HD
Source: Br J Nutr, 2014 Mar 14;111(5), p. 847-53.
EPub date: 2013 Oct 7.
Regression calibration in nutritional epidemiology: example of fat density and total energy in relationship to postmenopausal breast cancer.
Authors: Prentice RL, Pettinger M, Tinker LF, Huang Y, Thomson CA, Johnson KC, Beasley J, Anderson G, Shikany JM, Chlebowski RT, Neuhouser ML
Source: Am J Epidemiol, 2013 Dec 1;178(11), p. 1663-72.
EPub date: 2013 Sep 24.
Case-only method for cause-specific hazards models with application to assessing differential vaccine efficacy by viral and host genetics.
Authors: Dai JY, Li SS, Gilbert PB
Source: Biostatistics, 2014 Jan;15(1), p. 196-203.
EPub date: 2013 Jun 27.
Calibrated predictions for multivariate competing risks models.
Authors: Gorfine M, Hsu L, Zucker DM, Parmigiani G
Source: Lifetime Data Anal, 2014 Apr;20(2), p. 234-51.
EPub date: 2013 May 31.
Factors relating to eating style, social desirability, body image and eating meals at home increase the precision of calibration equations correcting self-report measures of diet using recovery biomarkers: findings from the Women's Health Initiative.
Authors: Mossavar-Rahmani Y, Tinker LF, Huang Y, Neuhouser ML, McCann SE, Seguin RA, Vitolins MZ, Curb JD, Prentice RL
Source: Nutr J, 2013 May 16;12, p. 63.
EPub date: 2013 May 16.
Health risks and benefits from calcium and vitamin D supplementation: Women's Health Initiative clinical trial and cohort study.
Authors: Prentice RL, Pettinger MB, Jackson RD, Wactawski-Wende J, Lacroix AZ, Anderson GL, Chlebowski RT, Manson JE, Van Horn L, Vitolins MZ, Datta M, LeBlanc ES, Cauley JA, Rossouw JE
Source: Osteoporos Int, 2013 Feb;24(2), p. 567-80.
EPub date: 2012 Dec 4.
Expected estimating equations via EM for proportional hazards regression with covariate misclassification.
Authors: Wang CY, Song X
Source: Biostatistics, 2013 Apr;14(2), p. 351-65.
EPub date: 2012 Nov 23.
Time-varying coefficient proportional hazards model with missing covariates.
Authors: Song X, Wang CY
Source: Stat Med, 2013 May 30;32(12), p. 2013-30.
EPub date: 2012 Oct 9.
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.
Simultaneously testing for marginal genetic association and gene-environment interaction.
Authors: Dai JY, Logsdon BA, Huang Y, Hsu L, Reiner AP, Prentice RL, Kooperberg C
Source: Am J Epidemiol, 2012 Jul 15;176(2), p. 164-73.
EPub date: 2012 Jul 6.
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.
Conditional estimation after a two-stage diagnostic biomarker study that allows early termination for futility.
Authors: Koopmeiners JS, Feng Z, Pepe MS
Source: Stat Med, 2012 Feb 28;31(5), p. 420-35.
EPub date: 2012 Jan 12.
Bias correction in the hierarchical likelihood approach to the analysis of multivariate survival data.
Authors: Jeon J, Hsu L, Gorfine M
Source: Biostatistics, 2012 Jul;13(3), p. 384-97.
EPub date: 2011 Nov 15.