|Grant Number:||5P01CA134294-05 Interpret this number|
|Primary Investigator:||Lin, Xihong|
|Organization:||Harvard School Of Public Health|
|Project Title:||Statistical Informatics for Cancer Research|
DESCRIPTION (provided by applicant): We propose a Program Project, Statistical Informatics in Cancer Research, to tackle a series of problems motivated by the analysis of high dimensional data arising in population-based studies of cancer. This Program Project comprises three research projects and two cores. Project 1 focuses on spatio-temporal modeling of disease count data collected for administrative areas. The specific aims are motivated by problems encountered in epidemiological studies designed to monitor and assess health disparities. Our proposed methods address issues associated with administrative boundaries changing over time, sparse disease counts, spatial confounding, and heavy computational burdens for large data sets. Methods will be applied to data on U.S. breast cancer incidence from three state cancer registries, Boston-area premature mortality, and NCI SEER data. Project 2 is also motivated by spatially-indexed data related to cancer incidence and mortality, but the emphasis is on population surveillance and spatial cluster detection. Three of the specific aims of Project 2 are motivated by the analysis of NCI SEER data and one from a case/control study designed to assess spatial clustering in childhood leukemia. This dataset also includes individual level data on several genetic biomarkers of susceptibility. One sub-aim of this project assesses gene-space interaction by studying whether disease clustering patterns differ according to genetic polymorphisms. Project 3 focuses on methods for the analysis of very high dimensional genomic and proteomic biomarkers. Extensions to spatially indexed genomic data are also considered in Project 3. All of the aims of the three projects are closely integrated with the motivating real world cancer studies in which the investigators are involved. The three projects link thematically through a focus on population-based, observational studies in cancer, as well as technically through the consideration of high-dimensional correlated data (arising from different sources) that require advanced statistical and computing methods. Several specific techniques (e.g. spatio-temporal modeling, penalized likelihoods, False Discovery Rates, hidden Markov models) are shared between two and in some cases all three projects. The two cores consist of an Administrative Core and a Statistical Computing Core. The Administrative Core will coordinate the overall scientific direction and programmatic activities of Program, which will include short courses, a visitor program, dissemination of research results, and an external advisory committee. A Statistical Computing Core will ensure the development and dissemination of open access, good quality, user friendly software designed to implement the statistical methods developed in the Research Projects, which is the final Specific Aim of each of the three projects. The Program Director and Co-Director, Professors Louise Ryan and Xihong Lin, respectively, are internationally known biostatisticians with strong track records of academic administration.
Immediate versus deferred initiation of androgen deprivation therapy in prostate cancer patients with PSA-only relapse. An observational follow-up study.
Authors: Garcia-Albeniz X, Chan JM, Paciorek A, Logan RW, Kenfield SA, Cooperberg MR, Carroll PR, Hernán MA
Source: Eur J Cancer, 2015 May;51(7), p. 817-24.
EPub date: 2015 Mar 17.
SAS macro for causal mediation analysis with survival data.
Authors: Valeri L, VanderWeele TJ
Source: Epidemiology, 2015 Mar;26(2), p. e23-4.
Cause-specific risk of hospital admission related to extreme heat in older adults.
Authors: Bobb JF, Obermeyer Z, Wang Y, Dominici F
Source: JAMA, 2014 Dec 24-31;312(24), p. 2659-67.
Short-term airborne particulate matter exposure alters the epigenetic landscape of human genes associated with the mitogen-activated protein kinase network: a cross-sectional study.
Authors: Carmona JJ, Sofer T, Hutchinson J, Cantone L, Coull B, Maity A, Vokonas P, Lin X, Schwartz J, Baccarelli AA
Source: Environ Health, 2014 Nov 13;13, p. 94.
EPub date: 2014 Nov 13.
Association between the Medicare hospice benefit and health care utilization and costs for patients with poor-prognosis cancer.
Authors: Obermeyer Z, Makar M, Abujaber S, Dominici F, Block S, Cutler DM
Source: JAMA, 2014 Nov 12;312(18), p. 1888-96.
Why post-progression survival and post-relapse survival are not appropriate measures of efficacy in cancer randomized clinical trials.
Authors: García-Albéniz X, Maurel J, Hernán MA
Source: Int J Cancer, 2015 May 15;136(10), p. 2444-7.
EPub date: 2014 Nov 3.
Learning how to improve healthcare delivery: the Swedish Quality Registers.
Authors: Adami HO, Hernán MA
Source: J Intern Med, 2015 Jan;277(1), p. 87-9.
EPub date: 2014 Nov 24.
Evaluation of the duplication of staging CT scans for localized colon cancer in a Medicare population.
Authors: García-Albéniz X, Logan RW, Schrag D, Hernán MA
Source: Med Care, 2014 Nov;52(11), p. 963-8.
Mediation analysis when a continuous mediator is measured with error and the outcome follows a generalized linear model.
Authors: Valeri L, Lin X, VanderWeele TJ
Source: Stat Med, 2014 Dec 10;33(28), p. 4875-90.
EPub date: 2014 Sep 14.
Effect of flexible sigmoidoscopy screening on colorectal cancer incidence and mortality: a randomized clinical trial.
Authors: Holme Ř, Lřberg M, Kalager M, Bretthauer M, Hernán MA, Aas E, Eide TJ, Skovlund E, Schneede J, Tveit KM, Hoff G
Source: JAMA, 2014 Aug 13;312(6), p. 606-15.
Rare-variant association analysis: study designs and statistical tests.
Authors: Lee S, Abecasis GR, Boehnke M, Lin X
Source: Am J Hum Genet, 2014 Jul 3;95(1), p. 5-23.
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.
Hospitalization burden and survival among older glioblastoma patients.
Authors: Arvold ND, Wang Y, Zigler C, Schrag D, Dominici F
Source: Neuro Oncol, 2014 Nov;16(11), p. 1530-40.
EPub date: 2014 Apr 28.
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.
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.
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.
Methodological challenges in mendelian randomization.
Authors: VanderWeele TJ, Tchetgen Tchetgen EJ, Cornelis M, Kraft P
Source: Epidemiology, 2014 May;25(3), p. 427-35.
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 Aug;43(4), p. 1294-313.
EPub date: 2014 Mar 16.
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.
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.
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.
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.
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.
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.
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.
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.
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.