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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 Multi-objective Optimal Experimental Designs for Event-related fMRI Studies
May 2 Jeff Gill Washington University Product Partitioned Dirichlet Process Prior Models for Identifying Substantive Clusters and Fitted Subclusters in Social Science Data
Apr 30 Jing Ning MD Anderson Cancer Center Statistical Inference on Right-censored Length-biased Data
Apr 23 Debajyoti Sinha Florida State University Safe Methods and Sample Size Determination for
Non-Inferiority Trial for Survival Response using a Proportional Odds Survival Model
Apr 16 Jianwen Cai University of North Carolina More Efficient Estimator for Additive Hazard Model for Case-Cohort Studies
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
«  September  2014 »
Events on September 4, 2014
  • Biostatistics Plan B Presentation
    Starts: 10:00 am
    Ends: September 4, 2014 - 11:00 am
    Location: Mayo Memorial Building, Room A301
    Description: Masters candidate in Biostatistics, Jason Xu, will present "Association Testing with the ADNI Sequence Data"

    Abstract: The whole genome sequence (WGS) data for 812 subjects from Alzheimer’s Disease Neuroimaging Initiative (ADNI) were processed and used for association testing. A gene-based sequence kernel association test (SKAT) and a single variant test were applied. For the gene-based testing by SKAT, none of the 19,219 genes was significant at the Bonferroni-adjusted significance level 2.6 × 10−6. Three genes THOC3, CDX1 and TMEM159 became significant if a less stringent significance level 5 × 10−5 was enforced. For the single variant testing, three common variants (kgp7502018, rs769449 and rs4420638) were found to be associated with the mean (left and right) hippocampal volume at the Bonferroni-adjusted genome-wide significance level 2.1 × 10−8, after 2,379,855 variants (1,293,243 common variants and 1,086,612 rare variants) were scanned. SNPs rs769449 and rs4420638 reside within gene ApoE and APoC1, respectively.
Events on September 17, 2014
  • Biostatistics Seminar
    Starts: 3:30 pm
    Ends: September 17, 2014 - 5:00 pm
    Location: Moos Tower, Room 2-520
    Description: Murray Clayton, of the Department of Statistics at the University of Wisconsin - Madison, will present.
Events on September 24, 2014
  • Biostatistics Seminar
    Starts: 3:30 pm
    Ends: September 24, 2014 - 5:00 pm
    Location: Moos Tower, Room 2-520
    Description: Jeff Goldsmith, of the Department of Biostatistics at Columbia University, will present "Generalized Multilevel Function-on-Scalar Regression and Principal Component Analysis"

    We considers regression models for generalized, multilevel functional responses: functions are generalized in that they follow an exponential family distribution and multilevel in that they are clustered within groups or subjects. This data structure is increasingly common across scientific domains and is exemplified by our motivating example, in which binary curves indicating physical activity or inactivity are observed for nearly six hundred subjects over five days. We use a generalized linear model to incorporate scalar covariates into the mean structure, and decompose subject-specific and subject-day-specific deviations using multilevel functional principal components analysis. Model parameters are estimated in a Bayesian framework using Stan, a programming language that implements a Hamiltonian Monte Carlo sampler. Simulations designed to mimic the application have good estimation and inferential properties with reasonable computation times for moderate datasets, in both cross-sectional and multilevel scenarios; code is publicly available. In the application we identify effects of age and BMI on the time-specific change in probability of being active over a twenty-four hour period.

    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
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