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Grant Details

Grant Number: 2P01CA134294-06 Interpret this number
Primary Investigator: Lin, Xihong
Organization: Harvard College
Project Title: Statistical Informatics for Cancer Research
Fiscal Year: 2013
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Abstract

This renewal application proposes to carry out a Program Project of statistical methods research to address gaps and barriers arising in the analysis of large and complex data from observational studies in cancer research. The ultimate goal of the Program is to use rich data sources to develop effective strategies for reducing cancer burden in the U.S. and improving longevity and quality of life. This Program Project comprises three research projects and two cores. The three integrated projects jointly address the statistical needs for three research priority areas identified by the Division of Cancer Contro and Population Science of National Cancer Institute: Health Disparities; Comparative Effectiveness Research; and Public Health Genomics. In Project 1, we will develop statistical methods to overcome common data limitations for the investigation of social and racial disparities spanning the cancer continuum. We will analyze data from the SEER database that is linked with data from the National Longitudinal Mortality Survey (NLMS). In Project 2, we will develop methods for comparative effectiveness research (CER) in cancer using large observational data. We will use the SEER-Medicare data and the CaPSURE cohort to emulate complex randomized trials to compare the effectiveness of personalized strategies for cancer diagnosis and dynamic strategies for cancer treatment. In Project 3, we will develop statistical methods for analysis of next generation sequencing data in genetic cancer epidemiological studies. The proposed research in Project 3 is motivated by and applied to the Harvard lung cancer and breast cancer exome and targeted sequencing studies as well as the affiliated Genome-Wide Association Studies. The Administrative Core will coordinate the overall scientific direction and programmatic activities of the Program, which will include regular P01 meetings, seminars, the annual retreat, the external advisory committee meeting, short courses, a visitor program, dissemination of research results. The Statistical Computing Core will allow access to Harvard largest high performance computing cluster, perform data management, and ensure the development and dissemination of open access, high quality software. The Program PIs, Professors Xihong Lin and Francesca Dominici, are renowned biostatisticians with strong track records of methodological and collaborative research and academic administration.

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Publications

Does exposure prediction bias health-effect estimation?: The relationship between confounding adjustment and exposure prediction.
Authors: Cefalu M, Dominici F
Source: Epidemiology, 2014 Jul;25(4), p. 583-90.
PMID: 24815302
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Maximizing the power of principal-component analysis of correlated phenotypes in genome-wide association studies.
Authors: Aschard H, Vilhjálmsson BJ, Greliche N, Morange PE, Trégouët DA, Kraft P
Source: Am J Hum Genet, 2014 May 1;94(5), p. 662-76.
EPub date: 2014 Apr 17.
PMID: 24746957
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JOINT ANALYSIS OF SNP AND GENE EXPRESSION DATA IN GENETIC ASSOCIATION STUDIES OF COMPLEX DISEASES.
Authors: Huang YT, Vanderweele TJ, Lin X
Source: Ann Appl Stat, 2014 Mar 1;8(1), p. 352-376.
PMID: 24729824
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Uncertainty in Propensity Score Estimation: Bayesian Methods for Variable Selection and Model Averaged Causal Effects.
Authors: Zigler CM, Dominici F
Source: J Am Stat Assoc, 2014 Jan 1;109(505), p. 95-107.
PMID: 24696528
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Methodological challenges in mendelian randomization.
Authors: VanderWeele TJ, Tchetgen Tchetgen EJ, Cornelis M, Kraft P
Source: Epidemiology, 2014 May;25(3), p. 427-35.
PMID: 24681576
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50-year trends in US socioeconomic inequalities in health: US-born Black and White Americans, 1959-2008.
Authors: Krieger N, Kosheleva A, Waterman PD, Chen JT, Beckfield J, Kiang MV
Source: Int J Epidemiol, 2014 Mar 16;null, p. null.
EPub date: 2014 Mar 16.
PMID: 24639440
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Ancestry estimation and control of population stratification for sequence-based association studies.
Authors: Wang C, Zhan X, Bragg-Gresham J, Kang HM, Stambolian D, Chew EY, Branham KE, Heckenlively J, FUSION Study, Fulton R, Wilson RK, Mardis ER, Lin X, Swaroop A, Zöllner S, Abecasis GR
Source: Nat Genet, 2014 Apr;46(4), p. 409-15.
EPub date: 2014 Mar 16.
PMID: 24633160
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National trends in pancreatic cancer outcomes and pattern of care among Medicare beneficiaries, 2000 through 2010.
Authors: Wang Y, Schrag D, Brooks GA, Dominici F
Source: Cancer, 2014 Apr 1;120(7), p. 1050-8.
EPub date: 2013 Dec 30.
PMID: 24382787
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Omnibus risk assessment via accelerated failure time kernel machine modeling.
Authors: Sinnott JA, Cai T
Source: Biometrics, 2013 Dec;69(4), p. 861-73.
EPub date: 2013 Nov 6.
PMID: 24328713
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GEE-based SNP set association test for continuous and discrete traits in family-based association studies.
Authors: Wang X, Lee S, Zhu X, Redline S, Lin X
Source: Genet Epidemiol, 2013 Dec;37(8), p. 778-86.
EPub date: 2013 Oct 25.
PMID: 24166731
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Gene set analysis using variance component tests.
Authors: Huang YT, Lin X
Source: BMC Bioinformatics, 2013 Jun 28;14, p. 210.
EPub date: 2013 Jun 28.
PMID: 23806107
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Consistent Group Identification and Variable Selection in Regression with Correlated Predictors.
Authors: Sharma DB, Bondell HD, Zhang HH
Source: J Comput Graph Stat, 2013 Apr 1;22(2), p. 319-340.
PMID: 23772171
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General framework for meta-analysis of rare variants in sequencing association studies.
Authors: Lee S, Teslovich TM, Boehnke M, Lin X
Source: Am J Hum Genet, 2013 Jul 11;93(1), p. 42-53.
EPub date: 2013 Jun 13.
PMID: 23768515
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Cross-ratio estimation for bivariate failure times with left truncation.
Authors: Hu T, Lin X, Nan B
Source: Lifetime Data Anal, 2014 Jan;20(1), p. 23-37.
EPub date: 2013 May 23.
PMID: 23700275
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Sequence kernel association tests for the combined effect of rare and common variants.
Authors: Ionita-Laza I, Lee S, Makarov V, Buxbaum JD, Lin X
Source: Am J Hum Genet, 2013 Jun 6;92(6), p. 841-53.
EPub date: 2013 May 16.
PMID: 23684009
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Genome-wide association analysis for multiple continuous secondary phenotypes.
Authors: Schifano ED, Li L, Christiani DC, Lin X
Source: Am J Hum Genet, 2013 May 2;92(5), p. 744-59.
PMID: 23643383
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Parallelism, uniqueness, and large-sample asymptotics for the Dantzig selector.
Authors: Dicker L, Lin X
Source: Can J Stat, 2013 Mar 1;41(1), p. 23-35.
PMID: 23589664
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Exposure to airborne particulate matter is associated with methylation pattern in the asthma pathway.
Authors: Sofer T, Baccarelli A, Cantone L, Coull B, Maity A, Lin X, Schwartz J
Source: Epigenomics, 2013 Apr;5(2), p. 147-54.
PMID: 23566092
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