Biostatistics Events

Biostatistics Departmental Calendar

Event
Mon 6/1/2020 11:00AM - 1:00PM
Biostatistics Dissertation Defense
Jun Zhang-Interpretable Analysis of Multivariate Functional Data-ONLINE Biostatistics Dissertation Defense
Jun Zhang-Interpretable Analysis of Multivariate Functional Data-ONLINE
Mon 6/1/2020 11:00AM - 1:00PM
** Online/Virtual Event **

Jun Zhang of the Department of Biostatistics defends her dissertation on "Interpretable Analysis of Multivariate Functional Data". 


** Online/Virtual Event **
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

Yongli Shuai: Multinomial Logistic Regression and Prediction Accuracy for Interval-Censored...

Friday 12/8 11:30AM - 1:30PM
7139 Public Health, Peterson Seminar Room

Yongli Shuai of the Department of Biostatistics defends his dissertation on "Multinomial Logistic Regression and Prediction Accuracy for Interval-Censored Competing Risks Data".

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


ABSTRACT:

Interval-censored competing risks data are ubiquitous in biomedical research fields. The direct parametric modeling of the cumulative incidence functional (CIF) is appealing due to its intuitive probability interpretation and easy implementation. This dissertation is to study and extend the multinomial logistic regression (MLR) model to interval-censored competing risks data. The MLR model naturally guarantees the additivity property of the event-specific probabilities under competing risks. A cubic B-Spline-based sieve method is then adopted to add flexibility into the proposed MLR model. The second study objective is to develop the prediction error (PE) as a model-free metric to evaluate and validate the prediction accuracy for interval censored competing risks data. Adopting the method of the pseudo-value estimator, this dissertation work proposes a novel approach to estimate the PE under the interval censored competing risks setting. Simulation studies are presented to assess performance of the MLR model and the PE in different scenarios. The proposed methods were then applied to a community-based study of cognitive impairment in aging population.

Public Health Significance: Interval-censored competing risks data could be often encountered in biomedical research that is essential for public health, such as rehabilitation and pain medicine. The proposed methods provide precise yet flexible modeling of such data with straightforward interpretation on how predictors affect the CIF, as well as useful tools to evaluate and validate the prediction accuracy of the developed models.

 

Last Updated On Monday, April 9, 2018 by Valenti, Renee Nerozzi
Created On Tuesday, November 7, 2017

AprMay 2020Jun
SunMonTueWedThuFriSat
262728293012
3456789
10111213141516
17181920212223
24252627282930
31123456

Submit events and news

Click to enter calendar events or share news and announcements.