Department of Biostatistics
Quantitative Issues in Cancer Research Working Seminar

2013 - 2014

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.

September 26

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

"Basic concepts and applications of Mendelian Risk prediction in cancer"

October 17

Lorenzo Trippa, Ph.D.
Assistant Professor, Department of Biostatistics, Harvard School of Public Health, and the Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute

"Cross Study Validation for Prediction Models"

October 31

Tianxi Cai, Sc.D.
Professor, Department of Biostatistics, Harvard School of Public Health

"Landmark Prediction and Estimation of Survival"

ABSTRACT: In recent years, genetic and biological markers have been examined extensively for their potential to signal progression or risk of disease. In addition to these markers, it has often been argued that short term outcomes may be helpful in making a better prediction of disease outcomes in clinical practice. Due to the potential difference in the underlying disease process, patients who have experienced a short term event of interest may have very different long term clinical outcomes from the general patient population. Most existing methods for incorporating censored short term event information in predicting long term survival focus on modeling the disease process and are derived under parametric models in a multi-state survival setting. In this talk, I will discuss prediction and estimation procedures that incorporating short term event time information up to a landmark point along with baseline covariates.
November 21

Michael Love, Ph.D.
Postdoctoral Fellow, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute

"Robust Estimates of Differential Expression at the Gene Level"

December 19

Donna Neuberg, Sc.D.
Senior Lecturer on Biostatistics, Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute

"The Answer is the Question: a Statistician's Perspective on Issues of Omics in Oncology"

ABSTRACT: High dimensional data associated with the various genomic and other approaches are arriving at our doors frequently, whether we are biostatistician, bioinformatricians, or computational biologists. I will review some of my experiences, and explain why I think garden-variety biostatisticians have a role to play in the analyses of these data.
February 6

Mehmet Samur, Ph.D.
Department of Biostatistics and Medical Informatics, Akdeniz University, Antalya, Turkey

"canEvolve: a web portal for integrative oncogenomics"

BACKGROUND & OBJECTIVE: Genome-wide profiles of tumors obtained using functional genomics platforms are being deposited to the public repositories at an astronomical scale, as a result of focused efforts by individual laboratories and large projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium. Consequently, there is an urgent need for reliable tools that integrate and interpret these data in light of current knowledge and disseminate results to biomedical researchers in a user-friendly manner. We have built the canEvolve web portal to meet this need.

RESULTS: canEvolve query functionalities are designed to fulfill most frequent analysis needs of cancer researchers with a view to generate novel hypotheses. canEvolve stores gene, microRNA (miRNA) and protein expression profiles, copy number alterations for multiple cancer types, and protein-protein interaction information. canEvolve allows querying of results of primary analysis, integrative analysis and network analysis of oncogenomics data. The querying for primary analysis includes differential gene and miRNA expression as well as changes in gene copy number measured with SNP microarrays. canEvolveprovides results of integrative analysis of gene expression profiles with copy number alterations and with miRNA profiles as well as generalized integrative analysis using gene set enrichment analysis. The network analysis capability includes storage and visualization of gene co-expression, inferred gene regulatory networks and protein-protein interaction information. Finally,canEvolve provides correlations between gene expression and clinical outcomes in terms of univariate survival analysis.

CONCLUSION: At present canEvolve provides different types of information extracted from 90 cancer genomics studies comprising of more than 10,000 patients. The presence of multiple data types, novel integrative analysis for identifying regulators of oncogenesis, network analysis and ability to query gene lists/pathways are distinctive features of canEvolve. canEvolve will facilitate integrative and meta-analysis of oncogenomics datasets.
February 20

Danielle Braun, AM
Doctoral Student, Department of Biostatistics, Harvard University

"Nonparametric Adjustment for Measurement Error in Time to Event Data"

ABSTRACT: Measurement error in time to event data used as a predictor will lead to inaccurate predictions. This arises in the context of self-reported family history, a time to event predictor often measured with error, used in Mendelian risk prediction models. Using a validation data set, we propose a method to adjust for this type of measurement error. We estimate the measurement error process using a nonparametric smoothed Kaplan-Meier estimator, and use Monte Carlo integration to implement the adjustment. We apply our method to simulated data in the context of both Mendelian risk prediction models and multivariate survival prediction models, as well as illustrate our method using a data application for Mendelian risk prediction models. Results from simulations are evaluated using measures of mean squared error of prediction (MSEP), area under the response operating characteristics curve (ROC-AUC), and the ratio of observed to expected number of events. These results show that our adjusted method mitigates the effects of measurement error mainly by improving calibration and by improving total accuracy. In some scenarios discrimination is also improved.
March 27

Rafael Irizarry, Ph.D.
Professor, Department of Biostatistics, Harvard School of Public Health, and the Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute

"Batch Effects"

April 10

Dianne Finkelstein, Ph.D.
Professor in the Department of Biostatistics, Department of Biostatistics, Harvard School of Public Health
Professor of Biostatistics in the Department of Medicine, HMS Director of Biostatistics, Mass General Hospital Cancer Center

"Joint Analysis of Progression and Survival with Missing Data from a Cancer Clinical Trial"

May 15

Corwin Zigler, Ph.D.
Assistant Professor, Department of Biostatistics, Harvard School of Public Health

"CER with Big Administrative Data: Bayesian Methods for Confounding Uncertainty and Heterogeneous Treatment Effects"

ABSTRACT: Comparative effectiveness research increasingly depends on the analysis of a rapidly expanding universe of observational data made possible by the growing integration of health care delivery, the dissemination of electronic medical records systems, and he development of clinical registries data. When attempting to make causal inferences with such large observational data structures, researchers are frequently confronted with decisions regarding which of a high-dimensional covariate set are necessary to properly adjust for confounding or to define subgroups experiencing heterogeneous treatment effects. This talk describes proposed Bayesian methods for propensity score variable selection and model averaging that 1) prioritize relevant variables from a set of candidate variables to include in the propensity score model for confounding adjustment and 2) estimate causal treatment effects as weighted averages of estimates under different models, where the associated weight for each model reflects the data-driven support for that model's ability to adjust for the necessary variables. Preliminary work to extend similar methods to detection and estimation of treatment effect heterogeneity will be discussed, and methods will be illustrated with a comparative effectiveness investigation of treatments for brain tumors among Medicare beneficiaries.

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