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Biostatistics Seminars

The Biostatistics seminar series includes research-focused talks by division faculty and other guests. All seminars are free and open to faculty, students and staff.

Upcoming and Recent Seminars

Date Speaker Affiliation Title
Sept 17 Murray Clayton University of Wisconsin – Madison Functional Concurrent Linear Regression Models for Spatial Images
Sep 24 Jeff Goldsmith Columbia University Generalized Multilevel Function-on-Scalar Regression and Principal Component Analysis
Oct 1 Wenbin Lu NC State University On Estimation of Optimal Treatment Regimes for Maximizing t-Year Survival Probability
Oct 22 Dean Follmann National Institute of Allergy and Infectious Disease Vaccine Effciacy from the Virion’s Perspective
Oct 29 Rebecca C. Steorts Carnegie Mellon University Methods for Quantifying Conflict Casualties in Syria
Nov 5 Felicity Enders Mayo Clinic Currently Unavailable
Dec 3 Shili Lin Ohio State University Robust Partial Likelihood Approach for Detecting Imprinting and Maternal Effects using Case-control Families
«  October  2014 »
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Events on October 1, 2014
  • Biostatistics Seminar
    Starts: 3:30 pm
    Ends: October 1, 2014 - 5:00 pm
    Location: Moos Tower, Room 2-520
    Description: Wenbin Lu, of the Department of Statistics at NC State University, will present "On Estimation of Optimal Treatment Regimes for Maximizing t-Year Survival Probability"

    Abstract:
    A treatment regime is a deterministic function that dictates personalized treatment based on patients' individual prognostic information. There is a fast-growing interest in finding optimal treatment regimes to maximize expected long-term clinical outcomes of patients for complex diseases, such as cancer and AIDS. For many clinical studies with survival time as a primary endpoint, a main goal is to maximize patients's survival probabilities given treatments. In this article, we first propose two nonparametric estimators for survival function of patients following a given treatment regime, i.e. the value function. Since the value function is very bumpy, kernel smoothing is introduced for estimating the optimal treatment regime within a class of pre-specified regimes by maximizing the smoothed value function. The estimation of optimal regimes for both single and multiple decision time points are studied. The asymptotic properties of the proposed estimators of value functions are established under suitable regularity conditions. Simulations are conducted to evaluate the numerical performance of the proposed estimators under various scenarios. An application to an AIDS clinical trial data is also given to illustrate the methods.


    A social tea will be held at 3:00 P.M. in A434 Mayo. All are Welcome.
    For more details contact 612-624-4655 or see http://www.sph.umn.edu/biostatistics/seminars/
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Events on October 16, 2014
  • Working Group in Imaging Meeting
    Starts: 1:30 pm
    Ends: October 16, 2014 - 2:30 pm
    Location: Mayo Memorial Building, Room A434
    Description: Fall meeting of the Working Group in Imaging.
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Events on October 22, 2014
  • Biostatistics Seminar
    Starts: 3:30 pm
    Ends: October 22, 2014 - 5:00 pm
    Location: Moos Tower, Room 2-520
    Description: Dean Follmann, of the Biostatistics Research Branch at the National Institute of Allergy and Infectious Diseases, will present "Vaccine Effciacy from the Virion’s Perspective"

    Abstract:
    Vaccine clinical trials traditionally use the time to consequential human infection as the primary endpoint. A common method of analysis for such trials is to compare the times to infection between the vaccine and placebo groups using a Cox regression model. With new technology, we can additionally record the precise number of virions that cause infection rather than just the indicator that infection occurred.

    In this paper we develop a unified approach for vaccine trials that couples the time to infection with the number of infecting or founder viruses. We assume that the instantaneous risk of a potentially infectious exposure for individuals in the placebo and vaccine groups follows the same proportional intensity model. Following exposure, the number of founder viruses X* is assumed to be generated from some distribution on 0,1,…, which is allowed to be different for the two groups. Exposures that result in X*=0 are unobservable. We denote the placebo and vaccine means of X* by m and m D so that 1- D measures the proportion reduction in the mean number of infecting virions due to vaccination per exposure. We develop different semi-parametric methods of estimating D. We allow the distribution of X* to be Poisson or unspecified, and discuss how to incorporate covariates that impact the time to exposure and/or X*.

    Interestingly D, which is a ratio of untruncated means, can be reliably estimated using truncated data, even if the placebo & vaccine distributions of X* are completely unspecified. Simulations of vaccine clinical trials show that the method can reliably recover D in realistic settings. We apply our methods to an HIV vaccine trial conducted in injecting drug users.

    *This work is joint with Chiung-Yu Huang

    A social tea will be held at 3:00 P.M. in A434 Mayo. All are Welcome.
    For more details contact 612-624-4655 or see http://www.sph.umn.edu/biostatistics/seminars/
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