Biostatistics Events

Biostatistics Departmental Calendar

Event
Thu 10/24/2019 3:30PM - 4:30PM
Biostatistics Seminar Series
Proportional Win-Fractions Regression Models for Composite Outcomes - Lu Mao, U of Wisconsin-Madison Biostatistics Seminar Series
Proportional Win-Fractions Regression Models for Composite Outcomes - Lu Mao, U of Wisconsin-Madison
Thu 10/24/2019 3:30PM - 4:30PM
Public Health Lecture Hall (A115)

Lu Mao, PhD, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison.


Public Health Lecture Hall (A115)
Thu 10/31/2019 3:30PM - 4:30PM
Biostatistics Seminar Series
Estimation and Inference in Metabolomics - Chris McKennan, University of Pittsburgh Biostatistics Seminar Series
Estimation and Inference in Metabolomics - Chris McKennan, University of Pittsburgh
Thu 10/31/2019 3:30PM - 4:30PM
Public Health Lecture Hall (A115)

Chris McKennan, PhD, Department of Statistics, University of Pittsburgh.


Public Health Lecture Hall (A115)
Thu 11/7/2019 3:30PM - 4:30PM
Biostatistics Seminar Series
Caroline Groth, West Virginia University Biostatistics Seminar Series
Caroline Groth, West Virginia University
Thu 11/7/2019 3:30PM - 4:30PM
Public Health Lecture Hall (A115)

Caroline P Groth, PhD, Department of Biostatistics, School of Public Health, West Virginia University.


Public Health Lecture Hall (A115)
Thu 11/14/2019 3:30PM - 4:30PM
Biostatistics Seminar Series
Student Internship in Statistics Panel - ASA Pittsburgh Student Chapter Biostatistics Seminar Series
Student Internship in Statistics Panel - ASA Pittsburgh Student Chapter
Thu 11/14/2019 3:30PM - 4:30PM
Public Health Lecture Hall (A115)

There will be a Q&A session with students from the Biostatistics and Statistics departments who have completed internships. 


Public Health Lecture Hall (A115)
Thu 11/21/2019 3:30PM - 4:30PM
Biostatistics Seminar Series
Biostatistics Seminar - Speaker TBA Biostatistics Seminar Series
Biostatistics Seminar - Speaker TBA
Thu 11/21/2019 3:30PM - 4:30PM
Public Health Lecture Hall (A115)


Public Health Lecture Hall (A115)
Sun 3/22/2020 to Wed 3/25/2020
Biostatistics Conference
ENAR 2020 Spring Meeting of the International Biometric Society -- JW Marriott Nashville Biostatistics Conference
ENAR 2020 Spring Meeting of the International Biometric Society -- JW Marriott Nashville
Sun 3/22/2020 to Wed 3/25/2020


Meetings of the Eastern North American Region of the International Biometric Society (a.k.a. "ENAR meetings") are held in late March or early April each year and reflect the broad interests of the Society, including both quantitative techniques and application areas. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations.


Sat 8/1/2020 to Thu 8/6/2020
Biostatistics Conference
Joint Statistical Meetings - - JSM 2020, Philadelphia, PA Biostatistics Conference
Joint Statistical Meetings - - JSM 2020, Philadelphia, PA
Sat 8/1/2020 to Thu 8/6/2020


The Joint Statistical Meetings, known simply as "JSM", is the largest gathering of statisticians held annually in North American. Faculty and student presenters from the  Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations. Our students often receive top awards and participate in the affiliated career marketplace at the event.


Sun 3/14/2021 to Wed 3/17/2021
Biostatistics Conference
ENAR 2021 Spring Meeting of the International Biometric Society -- Baltimore Biostatistics Conference
ENAR 2021 Spring Meeting of the International Biometric Society -- Baltimore
Sun 3/14/2021 to Wed 3/17/2021


Meetings of the Eastern North American Region of the International Biometric Society (a.k.a. "ENAR meetings") are held in late March or early April each year and reflect the broad interests of the Society, including both quantitative techniques and application areas. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations.


Sat 8/7/2021 to Thu 8/12/2021
Biostatistics Conference
Joint Statistical Meetings - - JSM 2021, Seattle, WA Biostatistics Conference
Joint Statistical Meetings - - JSM 2021, Seattle, WA
Sat 8/7/2021 to Thu 8/12/2021


The Joint Statistical Meetings, known simply as "JSM", is the largest gathering of statisticians held annually in North American. Faculty and student presenters from the  Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations. Our students often receive top awards and participate in the affiliated career marketplace at the event.


Sun 3/27/2022 to Wed 3/30/2022
Biostatistics Conference
ENAR 2022 Spring Meeting of the International Biometric Society -- Houston Biostatistics Conference
ENAR 2022 Spring Meeting of the International Biometric Society -- Houston
Sun 3/27/2022 to Wed 3/30/2022


Meetings of the Eastern North American Region of the International Biometric Society (a.k.a. "ENAR meetings") are held in late March or early April each year and reflect the broad interests of the Society, including both quantitative techniques and application areas. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations.


Sat 8/6/2022 to Thu 8/11/2022
Biostatistics Conference
Joint Statistical Meetings - - JSM 2022, Washington, DC Biostatistics Conference
Joint Statistical Meetings - - JSM 2022, Washington, DC
Sat 8/6/2022 to Thu 8/11/2022


The Joint Statistical Meetings, known simply as "JSM", is the largest gathering of statisticians held annually in North American. Faculty and student presenters from the  Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations. Our students often receive top awards and participate in the affiliated career marketplace at the event.


Recent Events

Biostatistics Dissertation Defense

Shu Wang - Clustering Methods with Variable Selection for Data with Mixed Variable Types or...

Monday 4/15 11:00AM - 1:00PM
7139 Public Health, Peterson Seminar Room

Shu Wang of the Department of Biostatistics defends her dissertation on "Clustering Methods with Variable Selection for Data with Mixed Variable Types of Limits of Detection". 

Committee Chairpersons: Jonathan Yabes, PhD and Joyce Chang, PhD, Department of Medicine

Committee Members:

Stewart Anderson, PhD, Department of Biostatistics

Qi Mi, PhD, Department of Sports Medicine and Nutrition

Christopher Seymour, MD, MSc, Department of Critical Care Medicine

Graduate faculty of the University and all other interested parties are invited to attend


ABSTRACT:

Clustering has emerged as one of the most essential and popular techniques for discovering patterns in data. Although clustering methods continue to be developed and adapted to cope with increasing complexity in data, certain features in datasets could limit the utilization of the existing approaches. First, many of the existing clustering methods are only useful for data with a single variable type – either all continuous or all categorical – despite the abundance of data with mixed variable types especially in the biomedical field. Second, standard cluster analyses typically assume that variables were collected completely and accurately. However, measurements for clinical biomarker data are often subject to limits of detection (LOD). In addition to these limitations, researchers are recently getting more interest in variable importance due to the increasing number of variables that become available for clustering. To achieve the goal of variable selection, and overcome aforementioned challenges, this dissertation proposes a non-parametric clustering method with variable selection for datasets with mixed types of variables, as well as a Bayesian framework for model-based clustering method with variable selection and the ability to handle censored biomarker variables.

 In the first section, we propose a hybrid density- and partition-based (HyDaP) algorithm for mixed variable types to simultaneously find the clusters and the variables that are most important for clustering. The HyDaP algorithm involves two steps: variable selection step and clustering step. In the variable filtration step, we first identify the data clustering structure formed by continuous variables. We propose and define three cluster structures to aid in understanding the contribution of continuous variables to clustering. We then implemented variable filtration procedures tailored to the specific cluster structure in selecting relevant variables. The selected variables are used in the clustering step where we apply our proposed novel dissimilarity measure for mixed data type together with an existing clustering algorithm (e.g. K-medoids) to find the clusters. Simulations across various scenarios and cluster structures were conducted to examine the performance of our proposed method compared to commonly used clustering algorithms.

 In the second section, we propose a finite mixture model under the Bayesian framework to simultaneously conduct variable selection, account for biomarker LOD and obtain clustering results. Finite mixture models for clustering perform well when parametric assumptions are met. However, it typically relies on the EM algorithm which could be sensitive to the choice of initial values. Thus, we took a Bayesian approach to obtain parameter estimates and the cluster membership. In addition, we put a spike-and-slab type of prior on each variable to obtain variable importance. To account for LOD, we added one more step in Gibbs sampling that iteratively fills in biomarker values below or above LODs. The performance of this method was examined under the same simulation settings in the first section.

 PUBLIC HEALTH SIGNIFICANCE: Uncovering subgroups and patterns in the data play an important role in precision medicine. Cluster analysis is an essential tool to accomplish this. However clustering methods need to evolve to cope with increasing amount and complexity of data being collected. Our proposed clustering algorithms can be applied to electronic health record (EHR) data that may include mixed types of variables and biomarkers with limit of detection. It allows to discover patient subgroups and identify important variables for clustering at the same time.

Last Updated On Monday, September 23, 2019 by Valenti, Renee Nerozzi
Created On Monday, March 25, 2019

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