Biostatistics Events

Biostatistics Departmental Calendar

Event Category
Thu 9/12/2019 3:30PM - 4:30PM
Mingyao Li, University of Pennsylvania Biostatistics Seminar Series
Mingyao Li, University of Pennsylvania
Thu 9/12/2019 3:30PM - 4:30PM
Public Health Lecture Hall (A115)

Mingyao Li, PhD, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania.


Public Health Lecture Hall (A115)
Biostatistics
Seminar Series
Thu 9/19/2019 3:30PM - 4:30PM
Novartis Information Session Biostatistics Seminar Series
Novartis Information Session
Thu 9/19/2019 3:30PM - 4:30PM
Public Health Lecture Hall (A115)


Public Health Lecture Hall (A115)
Biostatistics
Seminar Series
Thu 10/10/2019 3:30PM - 4:30PM
Peter Mueller, University of Texas at Austin Biostatistics Seminar Series
Peter Mueller, University of Texas at Austin
Thu 10/10/2019 3:30PM - 4:30PM
Public Health Lecture Hall (A115)

Peter Mueller, PhD, Department of Mathematics, Department of Statistics and Data Sciences, University of Texas at Austin


Public Health Lecture Hall (A115)
Biostatistics
Seminar Series
Thu 10/24/2019 3:30PM - 4:30PM
Lu Mao, University of Wisconsin-Madison Biostatistics Seminar Series
Lu Mao, University 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)
Biostatistics
Seminar Series
Thu 11/7/2019 3:30PM - 4:30PM
Snehalata Huzurbazar, West Virginia University Biostatistics Seminar Series
Snehalata Huzurbazar, West Virginia University
Thu 11/7/2019 3:30PM - 4:30PM
Public Health Lecture Hall (A115)

Snehalata Huzurbazar, PhD, Department of Biostatistics, West Virginia University


Public Health Lecture Hall (A115)
Biostatistics
Seminar Series
Sun 3/22/2020 to Wed 3/25/2020
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.


Biostatistics
Conference
Sat 8/1/2020 to Thu 8/6/2020
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.


Biostatistics
Conference
Sun 3/14/2021 to Wed 3/17/2021
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.


Biostatistics
Conference
Sat 8/7/2021 to Thu 8/12/2021
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.


Biostatistics
Conference
Sun 3/27/2022 to Wed 3/30/2022
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.


Biostatistics
Conference
Sat 8/6/2022 to Thu 8/11/2022
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.


Biostatistics
Conference

Recent Events

Biostatistics Dissertation Defense

Shannon Woolley: "Tests for Random Signs Censoring in Competing Risks"

Monday 4/10 2:00PM - 4:00PM
7139 Public Health, Peterson Seminar Room

Shannon Woolley of the Department of Biostatistics defends her dissertation on "Tests for Random Signs Censoring in Competing Risks"

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


ABSTRACT:

In the setting of competing risks, the marginal survival functions of the latent failure times are nonidentifiable without making further assumptions about the joint distribution, the majority of which are untestable. One exception is the random signs censoring assumption which assumes the main event time is independent of the indicator that the main event preceded the competing event. Few methods exist to formally test this assumption, and none consider a stratified test, which detects whether random signs censoring is met within subgroups of a categorical covariate. We develop a nonparametric stratified test for random signs censoring that is easy to implement. In addition, it is often of interest to model the effects of several covariates in relation to the cause of interest. Thus, as an extension of the stratified test, we also propose a test for conditional random signs censoring, which allows for the random signs censoring assumption to be met after adjusting for categorical and/or continuous covariates. Through Monte Carlo simulations, we show our proposed test statistics have empirical levels close to the nominal level and maintain adequate power even with relatively small sample sizes and random right censoring. Compared to the standard test, both of our proposed tests have nearly equivalent power under random signs censoring and are superior in situations of stratified or conditional random signs censoring, where the standard test fails to detect random signs censoring within subgroups or after adjusting for covariates, respectively. Their ease of implementation and utility are illustrated through an application to liver transplant data from the United Network for Organ Sharing. Public Health Significance: Clinicians must make decisions affecting patients’ lives using the information available to them. Relying on research results based on models that use unverifiable assumptions can lead to inaccurate conclusions. The methods proposed here offer a solution to allow for more accurate modeling of marginal survival functions with competing risk data. Through use of these new methods, patient outcomes can be improved over time.

Last Updated On Friday, July 07, 2017 by Valenti, Renee Nerozzi
Created On Monday, March 13, 2017

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