|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.
HYPOTHESIS TESTING FOR HIGH-DIMENSIONAL SPARSE BINARY REGRESSION.
Authors: Mukherjee R, Pillai NS, Lin X
Source: Ann Stat, 2015 Feb;43(1), p. 352-381.
Improved ancestry estimation for both genotyping and sequencing data using projection procrustes analysis and genotype imputation.
Authors: Wang C, Zhan X, Liang L, Abecasis GR, Lin X
Source: Am J Hum Genet, 2015 Jun 4;96(6), p. 926-37.
EPub date: 2015 May 28.
Bayesian Semi-parametric Analysis of Semi-competing Risks Data: Investigating Hospital Readmission after a Pancreatic Cancer Diagnosis.
Authors: Lee KH, Haneuse S, Schrag D, Dominici F
Source: J R Stat Soc Ser C Appl Stat, 2015 Feb 1;64(2), p. 253-273.
Accounting for uncertainty in confounder and effect modifier selection when estimating average causal effects in generalized linear models.
Authors: Wang C, Dominici F, Parmigiani G, Zigler CM
Source: Biometrics, 2015 Apr 20;null, p. null.
EPub date: 2015 Apr 20.
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.
Reproductive justice and the pace of change: socioeconomic trends in US infant death rates by legal status of abortion, 1960-1980.
Authors: Krieger N, Gruskin S, Singh N, Kiang MV, Chen JT, Waterman PD, Gottlieb J, Beckfield J, Coull BA
Source: Am J Public Health, 2015 Apr;105(4), p. 680-2.
EPub date: 2015 Feb 25.
Alternative decompositions for attributing effects to interactions.
Authors: VanderWeele TJ, Tchetgen Tchetgen EJ
Source: Epidemiology, 2015 May;26(3), p. e32-4.
Discussion of "On Bayesian estimation of marginal structural models".
Authors: Robins JM, Hernán MA, Wasserman L
Source: Biometrics, 2015 Jun;71(2), p. 296-9.
EPub date: 2015 Feb 4.
SAS macro for causal mediation analysis with survival data.
Authors: Valeri L, VanderWeele TJ
Source: Epidemiology, 2015 Mar;26(2), p. e23-4.
Think globally, act globally: An epidemiologist's perspective on instrumental variable estimation.
Authors: Swanson SA, Hernán MA
Source: Stat Sci, 2014 Aug;29(3), p. 371-374.
Associations between long-term exposure to chemical constituents of fine particulate matter (PM2.5) and mortality in Medicare enrollees in the eastern United States.
Authors: Chung Y, Dominici F, Wang Y, Coull BA, Bell ML
Source: Environ Health Perspect, 2015 May;123(5), p. 467-74.
EPub date: 2015 Jan 6.
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.
On the analysis of hybrid designs that combine group- and individual-level data.
Authors: Smoot E, Haneuse S
Source: Biometrics, 2015 Mar;71(1), p. 227-36.
EPub date: 2014 Sep 22.
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.
Sparse kernel machine regression for ordinal outcomes.
Authors: Shen Y, Liao KP, Cai T
Source: Biometrics, 2015 Mar;71(1), p. 63-70.
EPub date: 2014 Sep 5.
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.
Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records.
Authors: Sinnott JA, Dai W, Liao KP, Shaw SY, Ananthakrishnan AN, Gainer VS, Karlson EW, Churchill S, Szolovits P, Murphy S, Kohane I, Plenge R, Cai T
Source: Hum Genet, 2014 Nov;133(11), p. 1369-82.
EPub date: 2014 Jul 26.
Attributing effects to interactions.
Authors: VanderWeele TJ, Tchetgen Tchetgen EJ
Source: Epidemiology, 2014 Sep;25(5), p. 711-22.