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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
May 7 Abhyuday Mandal University of Georgia TBD
Apr 30 Jing Ning MD Anderson Cancer Center TBD
Apr 23 Debajyoti Sinha Florida State University TBD
Apr 16 Jianwen Cai University of North Carolina TBD
Mar 26 Michael Rosenblum Johns Hopkins University Optimal Tests of Treatment Effects for the Overall Population and Two Subpopulations in Randomized Trials, using Sparse Linear Programming
Feb 26 Eric Vance Virginia Tech LISA 2020: Creating a Network of Statistical Collaboration Laboratories
Feb 11 Eric Lock Duke University Exploratory Methods for the Integrated Analysis of Multi-Source Data
Feb 7 Fan Yang University of Pennsylvania Using Post-Quality of Life Measurement Information in Censoring by Death Problems
Feb 5 Yingying Wei Johns Hopkins University Integrative Statistical Models for Genomic Signal Detection and Ultra-high-dimensional Prediction
Jan 31 Susan Wei University of North Carolina Latent Supervised Learning
Jan 29 Yong Chen University of Texas – Houston Novel Statistical Tests for Homogeneity in Semiparametric Mixture Models with Application to Methylation Data.
Jan 22 Prabhani K. Don Penn State Finding Latent Structures: Model-based Approaches and Applications in Genomics
Jan 15 Matt McCall University of Rochester Gene Regulatory Network Estimation
Dec 11 Bruce Swihart Johns Hopkins University Sleep Hypnograms, Insurance Claims, & Hand Movement After Stroke: Big Data and Potentially Many Weak, Predictive Signals
Dec 4 Bryan Shepherd Vanderbilt Comparing Different Methods for Assessing When-to-Start Treatment
Nov 20 Sijian Wang University of Wisconsin – Madison Regularized Outcome Weighted Subgroup Identification for Differential Treatment Effects
Nov 6 Doug Schaubel University of Michigan Semiparametric Methods for Survival Analysis of Case-control Data Subject to Dependent Censoring
Oct 30 Seunggeun Lee University of Michigan Rare Variant Analysis in Sequencing-based Association Studies
Oct 23 Rob Weiss UCLA The Markov Chinese Restaurant Process: A Non-parametric Bayesian Cluster Memory Model for Longitudinal Data
Oct 16 Daniela Witten University of Washington Graph Estimation with Joint Additive Models
Oct 2 Yong Chen University of Texas – Houston Regression Analysis of Longitudinal Data with Irregular and Informative Observation Times
Sep 25 John Fricks Penn State Stochastic Modeling and Inference of Molecular Motors across Scales
Sep 18 Nidhi Kohli University of Minnesota Fitting a Linear-linear Piecewise Growth Mixture Model with Unknown Knots: A Comparison of Different Software Procedures
«  April  2014 »
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Events on April 16, 2014
  • Biostatistics Seminar
    Starts: 3:30 pm
    Ends: April 16, 2014 - 5:00 pm
    Location: Moss Tower, room 2-620
    Description: Jianwen Cai, of the Department of Biostatistics at the University of North Carolina at Chapel Hill, will present: "More Efficient Estimator for Additive Hazard Model for Case-Cohort Studies".

    Abstract:
    The case-cohort study design has often been used in studies of a rare disease or for a common disease but with limited stored biospecimen from the participants. A case-cohort study design consists of a random sample, called the subcohort, and all or a portion of the subjects with the disease of interest. One advantage of the case-cohort design is that the same subcohort can be used for studying multiple diseases. In multiple case-cohort studies, covariates collected on subjects with other diseases are available when estimating the risk effect on one disease. Usually, the analysis is done separately for each disease ignoring data collected on subjects with the other diseases. We propose a more efficient estimator by making full use of available covariate information for the additive hazards model with data from case-cohort study designs. We propose an estimating equation approach with a new weight function. The proposed estimators are shown to be consistent and asymptotically normally distributed. Simulation studies show that the proposed method using all available information leads to efficiency gain. Our proposed method is applied to data from the Atherosclerosis Risk in Communities (ARIC) study.


    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 April 23, 2014
  • Biostatistics Seminar
    Starts: 3:30 pm
    Ends: April 23, 2014 - 5:00 pm
    Location: Lcoation to be determined.
    Description: Debajyoti Sinha, of the Department of Statistics at Florida State University, will present.
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Events on April 30, 2014
  • Biostatistics Seminar
    Starts: 3:30 pm
    Ends: April 30, 2014 - 5:00 pm
    Location: Moos Tower, room 2-620
    Description: Jing Ning, of the Department of Biostatistics at the University of Texas MD Anderson Cancer Center, will present: "Statistical Inference on Right-censored Length-biased Data".

    Abstract:
    Length-biased sampling has been well recognized in cancer screening studies, economics, industrial reliability and etiology applications. Length biased right-censored data have a unique data structure different from traditional survival data, and consequently nonparametric and semiparametric estimation and inference methods for traditional survival data are not directly applicable. One prominent challenge is the informative censoring induced by the biased sampling scheme. It has not been adequately addressed in literature, either by simply ignoring the dependent censoring or not allowing right censoring. In this talk, some of our most recent work will be presented, focusing on flexible semiparametric models to assess covariate effects on the population failure times given length-biased times. Estimating methods are developed and computing algorithms and asymptotic properties are established. The methods are evaluated through extensive simulations and illustrated by application to data from a prevalent cohort study of dementia patients.



    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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