Department of Biostatistics
Quantitative Issues in Cancer Research Working Seminar

2014 - 2015

Organizer: Christina McIntosh


Schedule: Thursdays, 12:30-2:00 p.m.
HSPH2, Room 426 (unless otherwise notified)

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Seminar Description
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.


October 9

Sebastien Haneuse, Ph.D.
Associate Professor, Department of Biostatistics, Harvard School of Public Health

"On the Analysis of Clustered Semi-competing Risks Data"
ABSTRACT: To monitor quality of care in the US, the Centers for Medicare and Medicaid Services (CMS) currently reports, among other measures, hospital-specific 30-day readmission rates, estimated on the basis of a logistic-Normal GLMM. The focus of these efforts is on health conditions with low mortality, including pneumonia and heart failure. Expanding these efforts to include a broad range of increasingly prevalent 'advanced' health conditions, such as Alzheimer's disease and cancer, is problematic because the current CMS approach ignores death as a truncating event. A more appropriate analysis would be to frame quality of care assessments within the semi-competing risk framework although, to our knowledge, no statistical methods for clustered semi-competing risks data have been developed. We propose a novel semi-parametric hierarchical model for clustered semi-competing data based on an illness-death model. Estimation and inference is within the Bayesian paradigm, which facilitates the use of hospital-specific shrinkage targets and flexible random effects distributions. An efficient computational algorithm is developed, based on the Metropolis-Hastings-Green algorithm. The proposed framework is then applied to data on all individuals diagnosed with pancreatic cancer between 2005-2008 from Medicare Part A.
October 30

Michael Love, Ph.D.
Research Fellow, Department of Biostatistics, Harvard School of Public Health / Dana-Farber Cancer Institute

"Technical Bias in Sequencing Data and Solutions"
ABSTRACT: High-throughput sequencing data (DNA-Seq, RNA-Seq, etc.) can be used for estimating biological properties of interest (copy number of a genomic region, RNA abundance, etc.) but is not free from technical bias. Most troubling, the effect of technical bias can vary from sample to sample, and across experimental batches. I will review a number of examples of such known technical biases, and discuss solutions for recovering accurate statistical inference.
November 13

Dimitrios Spentzos, M.D. M.M.Sc.
Assistant Professor, Department of Medicine, Harvard Medical School
Associate Director for Clinical Research, Proteomics Core, Dana Farber/Harvard Cancer Center, Beth Israel Deaconess Medical Center
Active Staff, Hematology/Oncology and Genomics Center, Beth Israel Deaconess Medical Center


"'Omics' Analysis for Outcome Prediction and Subtyping in Osteosarcoma"
ABSTRACT: Oteosarcoma is a cancer with significant clinical heterogeneity. Genome-wide, "omics" studies could be very useful in deciphering the poorly understood molecular context of this tumor and may aid in identifying robust molecular markers for disease course and outcome. Using microRNA analyses, we have discovered highly prognostic profiles and also have identified a specific genomic locus as potentially very important in disease outcome. Furthermore, our data support the notion that the microRNA profiles are not simply prognostic but may be surrogates for a distinct molecular osteosarcoma subtype, perhaps driven by deregulated biology of the emerging critical genomic locus. Studies are planned to 1) externally validate the value of microRNAs, and develop clinically useful prognostic tools 2) study the "sequenome" of these microRNAs and 3) study the methylation patterns that could be partly responsible for modulating distinct subtypes and the clinical course of osteosarcoma.
December 4

Philipp Altrock, Ph.D.
Research Fellow, Department of Biostatistics, Harvard School of Public Health / Dana-Farber Cancer Institute

"Talk Title TBD"
ABSTRACT: None Given
February 5

Franziska Michor, Ph.D.
Associate Professor of Computational Biology, Department of Biostatistics, Harvard School of Public Health / Dana-Farber Cancer Institute

"Talk Title TBD"
ABSTRACT: None Given
February 26

To Be Announced


"Talk Title TBD"
ABSTRACT: None Given
March 12

Steffen Ventz, Ph.D.
Research Fellow, Department of Biostatistics, Harvard School of Public Health / Dana-Farber Cancer Institute

"Talk Title TBD"
ABSTRACT: None Given
April 2

To Be Announced


"Talk Title TBD"
ABSTRACT: None Given
April 23

Christina McIntosh, Ph.D.
Doctoral Student, Department of Biostatistics, Harvard University

"Talk Title TBD"
ABSTRACT: None Given
May 14

Mehmet Samur, Ph.D.
Research Fellow, Department of Biostatistics, Harvard School of Public Health / Dana-Farber Cancer Institute

"Talk Title TBD"
ABSTRACT: None Given


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Last Update: November 12, 2014