There are more than one million new cancer cases every year in the United States. An additional 5-8 million people are living with cancer. Research on cancer has greatly influenced the development of statistical methods in the past two decades and is likely to continue to do so in the future. This working seminar will be a forum for the discussion of current methodologic developments as well as cancer research having a strong quantitative basis. The working seminars will include expository reviews of special topics as well as the presentation of new research. All students and faculty are invited to attend and participate.

Harald Weedon-Fekjær
Statistician, Department of Etiological Cancer Research, Cancer Registry of Norway, Norway

"Breast Cancer Tumour Growth Estimated Through Mammography Screening Data"

ABSTRACT: Mammography screening aims to reduce the number of breast cancer deaths, through earlier diagnosis/treatment. Together with screening test sensitivity, breast cancer tumour growth is important in the planning and the evaluation of mammography screening, but there exists only small clinical observational studies of breast cancer tumour growth, as almost all breast cancer in developed countries are treated. As an alternative, tumour development can be observed indirectly through variations in breast cancer incidence caused by screening. I will, in this lecture, present a new estimating procedure, and show how data from 395 188 women participating in the Norwegian Breast Cancer Screening program can be used to estimate tumour growth and screening test sensitivity directly linked to tumour size. (See attached file)

Giovanni Parmigiani, Ph.D.
Professor, Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute

"Interpreting Diverse Genomic Data Using Gene Sets"

ABSTRACT: Gene set analysis considers whether genes that form a set from a specific biological standpoint, also behave in a related way. It is commonly used in high throughput genomic experiments to help with interpretability of results. The cross referencing of genes to both sets and phenotypic variation is very powerful. Creative definition of sets has allowed combined analysis and interpretation of very disparate sources of knowledge. In this presentation I will provide a brief review of concepts, our ongoing research on models for gene set analysis, and remaining challenges.

Yi Li, Ph.D.
Associate Professor, Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute

"The Dantzig Selector for Censored Linear Regression Models: Identifying Predictive Genes for Myeloma Disease Progression"

ABSTRACT: The Dantzig variable selector has recently emerged as a powerful tool for fitting regularized regression models. A key advantage is that it does not pertain to a particular likelihood or objective function, as opposed to the existing penalized likelihood methods, and hence has the potential for wide applications. To our knowledge, all the Dantzig selector work has been performed with fully observed response variables. This talk introduces a new class of adaptive Dantzig variable selectors for linear regression models when the response variable is subject to right censoring. This is motivated by a clinical study of detecting predictive genes for myeloma patients' event-free survival, which is subject to right censoring. We establish the theoretical properties of our procedures, including consistency in model selection (i.e. the right subset model will be identified with a probability tending to 1) and the oracle property of the estimation (i.e. the asymptotic distribution of the estimates is the same as that when the true subset model is known a priori). The practical utility of the proposed adaptive Dantzig selectors is verified via extensive simulations. We apply the new method to the aforementioned myeloma clinical trial and identify important predictive genes for patients' event free survival.

Armin Schwartzman, Ph.D.
Assistant Professor, Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute

"Statistical Parametric Imaging for Assessing Response to Therapy in Neuro-Oncology"

ABSTRACT: Molecular-targeted therapy in cancer has been shown to demonstrate early changes in glucose metabolic activity within one week of treatment, as measured with FDG-PET using manual methods to identify and define tumor volumes. For longitudinal studies in neuro-oncology, a statistical methodology is proposed to automate the detection and characterization of the changes at the voxel level via parametric images of response. The proposed methodology involves image registration, image segmentation, background adjustment, and statistical comparison at each voxel.

This is joint work with Mengye Guo from the Dept. of Biostatistics and Comp. Biology at DFCI and Jeffrey Yap from the Dept. of Imaging at DFCI. This talk is a follow-up to the one given at the DF/HCC dinner in May of 2009.

Montserrat Rue, Ph.D.
Biomedical Research Institut of Lleida (IRBLLEIDA), Lleida, Catalonia, Spain

"Effectiveness of Early Detection on Breast Cancer Mortality Reduction in Catalonia (Spain)"

ABSTRACT: There has been increasing interest in using population data and mathematical models to assess the effectiveness of cancer early detection. We have used a probabilistic model developed by Sandra Lee and Marvin Zelen, under the CISNET initiative, to evaluate early detection of breast cancer in Catalonia (Spain). The Lee and Zelen model (LZ), estimates the cumulative probability of death from breast cancer for a cohort exposed to any screening program or to routine health care, after T years of follow-up. Then these probabilities are used to estimate possible mortality reduction. In this seminar I will present the statistical methods used to obtain the inputs of the model as well as the estimates of effectiveness of different screening strategies. Other important issues, such as cost-effectiveness of early detection and overdiagnosis of breast cancer, will also be discussed.

Hajime Uno, Ph.D.
Research Scientist, Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute

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Francesca Dominici, Ph.D.
Professor, Department of Biostatistics, Harvard School of Public Health

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David Christiani, M.D., MPH
Professor of Occupational Medicine and Epidemiology, Departments of Environmental Health and Epidemiology, Harvard School of Public Health

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Cheng Li, Ph.D.
Associate Professor, Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute

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Guocheng Yuan, Ph.D.
Assistant Professor of Computational Biology and Bioinformatics, Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute

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Xiaole Shirley Liu, Ph.D.
Associate Professor, Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute

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