Home » Biometrics, Member News, Section News

Biometrics Section Readies for JSM, Looks Forward to Other Meetings

1 May 2013 141 views No Comment
Edited by Feifei Wei, Biometrics Section Publications Officer

    The Biometrics Section will sponsor the following six short courses during the 2013 Joint Statistical Meetings in Montréal:

    • Statistical Methods in Genetic Association Studies, taught by Danyu Lin of The University of North Carolina
      Association studies have become the primary tool for the genetic dissection of complex human diseases, and genetic association has played an increasingly important role in biomedical research. This course provides an overview of the statistical methods that have been recently developed for the designs and analysis of genetic association studies. Specific topics include genome-wide association studies, case-control sampling and retrospective likelihood, secondary phenotypes in case-control studies, haplotypes and untyped SNPs, population stratification, meta-analysis, multiple testing, next-generation sequencing studies, rare variants, trait-dependent sampling, variable selection, and risk prediction.
    • Personalized Medicine and Dynamic Treatment Regimes, taught by Michael Kosorok of The University of North Carolina and Eric Labler of North Carolina State University
      This course will introduce the basic concepts and methods for discovery of individualized treatment regimes, including dynamic treatment regimes, based on clinical data. Treatment regimens are rules that assign treatment to patients based on patient-level prognostic information, including genetic information and clinical response to past treatment. This is distinct from more traditional approaches to personalized medicine, which seek to develop new treatments tailored to a specific subgroup of patients with unique biochemical characteristics. In contrast to this, the treatment regimens we will describe seek to find the best treatment for each and every patient. Dynamic treatment regimens extend this idea to settings in which treatment decisions are made at multiple time points, such as is the case for cancer treatment that consists of multiple lines, and the goal is to determine the best treatment at each time point to maximize long-term clinical outcome. We also will discuss several new statistical learning tools, including Q-learning and other more recent developments, which have proven to be powerful assets in solving this important class of problems. Techniques for designing phase II and phase III clinical trials for discovery and verification of individualized treatment regimens also will be discussed and illustrated with several practical clinical examples.
    • Analysis of Interval-Censored Survival Data, taught by Philip Hougaard of Lundbeck
      Interval-censored survival data occur when the time to an event is assessed by means of blood samples, urine samples, X-ray, or other screening methods that cannot tell the exact time of change for the disease, but only that the change has happened since the previous examination. This is in contrast to standard thinking that assumes the change happens at the time of the first positive examination. Even though this screening setup is common and methods to handle such data nonparametrically in the one-sample case were suggested more than 25 years ago, it is still not a standard method. However, interval-censored methods are needed to consider onset and diagnosis as different, such as when we consider screening to diagnose a disease earlier. The reason for the low use of interval-censored methods is that in the nonparametric case, analysis is technically more complicated than standard survival methods based on exact times. The same applies to proportional hazards models. I will give an introduction to this type of data, including a discussion of the issues. The statistical theory will not be dealt with in detail, but high-level differences to results for standard right-censored survival data will be presented. Both parametric, nonparametric, and semiparametric (proportional hazards) models will be covered. I will emphasize applications, using examples from the literature and my own experience regarding development of microalbuminuria among Type 2 diabetic patients.
    • Statistical Methods for Medical Imaging Analysis, taught by Hongtu Zhu and Haipeng Shen of The University of North Carolina
      With modern imaging techniques, massive imaging data can be observed over both time and space (e.g., magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI)). The subject of medical imaging analysis has exploded from simple algebraic operations on imaging data to advanced statistical and mathematical methods on imaging data. This short course aims to provide a practical introduction to and an overview of recent advanced statistical development to analyze and model medical image data quantitatively. The course material is applicable to a wide variety of medical and biological imaging problems. The topics cover tract-based analysis, multi-scaled statistical methods, fMRI processing methods, diffusion imaging methods, brain image, and genetics. While presenting the statistical and mathematical fundamentals, we emphasize the concepts, methods, and their real-world implementation. Participants will learn basics that will help them understand the methods and tools built into packages like SPM, FSL, Slicers, and others to optimally use them.
    • Statistical Evaluation of Prognostic Biomarkers, taught by Patrick J. Heagerty of the University of Washington and Paramita Saha-Chaudhuri of Duke University
      Longitudinal studies allow investigators to correlate changes in time-dependent exposures or biomarkers with subsequent health outcomes. The use of baseline or time-dependent markers to predict a subsequent change in clinical status such as transition to a diseased state requires the formulation of appropriate classification and prediction error concepts. Similarly, the evaluation of markers that could be used to guide treatment require specification of operating characteristics associated with use of the marker. The first part of this course will introduce predictive accuracy concepts that allow evaluation of time-dependent sensitivity and specificity for prognosis of a subsequent event time. We will overview options that are appropriate for both baseline markers and longitudinal markers. Methods will be illustrated using examples from HIV and cancer research. The second part of this course will involve a technology workshop that will introduce the R packages (survivalROC, risksetROC, compriskROC) currently available for predictive accuracy of survival model. This segment will include hands-on training and demonstration of how to use these R packages for answering research questions. Several real-data examples for analysis will be provided, and the instructors will discuss implementation and interpretation.
    • Practical Software Engineering for Statisticians, taught by Murray Stokely of Google
      Statisticians are increasingly being employed alongside software engineers to make sense of the large amounts of data collected in modern e-commerce, Internet, retail, and advertising companies. This course introduces a number of best practices in writing statistical software taught to computer scientists, but which is seldom part of a statistics degree. Revision control tools, unit testing, code modularity, structure and readability, and the basics of computer architecture and performance will be covered. A few examples of real R code written in a commercial environment will be shared and discussed to illustrate some of the problems of moving from working alone or in a small group in an academic setting into a team in a large commercial setting (the course is mostly language agnostic, but R will be used in examples).

    Invited Sessions

    In addition to the CE courses, the Biometrics Section also will sponsor the following six invited sessions:

    • Current Statistical Issues in Comparative Effectiveness Research, organized by Haibo Zhou of The University of North Carolina
    • Dynamic Treatment Regimes and Adaptive Designs Toward Personalized Health Care, organized by Lu Wang of the University of Michigan
    • Emerging Statistical Methods for Big Data, organized by Ping Ma, University of Illinois at Urbana-Champaign
    • Frontiers in Longitudinal and Survival Data Analysis, organized by Gang Li of the University of California at Los Angeles
    • Big Data, Big Impact When Statistics Matter, organized by Ching-Ti Liu of Boston University
    • Questions in Cancer Research: What Are the Most Pressing Statistical Problems?, organized by Michelle Dunn of the National Cancer Institute

    ENAR 2014

    It is time to think about invited sessions for ENAR 2014, which will be held March 16–19, in Baltimore, Maryland. Anyone interested in organizing an invited session or who has ideas for one should contact the 2014 Biometrics Section representative, Jason Roy, at jaroy@mail.med.upenn.edu.

    A typical session consists of three 30-minute talks followed by a discussion or four 25-minute talks. June 22 is the deadline for proposals. It is best if you have a well-defined topic and commitments from participants by June 22. The more detailed the proposal, the better the chances it will be selected in this highly competitive process.

    JSM 2014

    It’s also time to start thinking about invited sessions for next year’s Joint Statistical Meetings, which will be held August 2–7 in Boston, Massachusetts. Anyone interested in organizing an invited session or who has ideas for one should contact the section’s 2014 program chair, Jonathan Schildcrout, at jonathan.schildcrout@vanderbilt.edu.

    A typical invited session consists of three 30-minute talks followed by a 10-minute invited discussion and 10 minutes of floor discussion. However, other formats are possible. The 2013 program is a good source for examples.

    Remember, the most mature ideas will have an advantage when competing for the limited number of slots, so it’s best to have your ideas in final form by the middle of June. The Biometrics Section will have at least four invited sessions, but will be able to compete for additional slots if we generate enough good ideas.

    Please also submit ideas for short courses to our 2013–2014 Continuing Education chair, Donglin Zeng, at dzeng@email.unc.edu.

    1 Star2 Stars3 Stars4 Stars5 Stars (No Ratings Yet)

    Comments are closed.