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

Ying Shan: Statistical Methods for Genetic Risk Confidence Intervals, Bayesian Disease Risk...

Tuesday 7/12 9:30AM - 11:30AM
A425 Public Health

Ying Shan of the Department of Biostatistics defends her dissertation on "Statistical Methods for Genetic Risk Confidence Intervals, Bayesian Disease Risk Prediction, and Estimating Mutation Screen Saturation" 

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

 


ABSTRACT:
Genetic information can be used to improve disease risk estimation as well as to estimate the number of genes influencing a trait.  Here I explore these issues in three parts. 1) For an informed understanding of a disease risk prediction, the confidence interval of the risk estimate should be taken into account. But few previous studies did. I constructed a better risk prediction model and provided a better screening strategy by taking the confidence interval into account.  Risk models are built with varying numbers of genetic risk variants known as single nucleotide polymorphisms (SNPs). Inclusion in the risk model of SNPs, sorted in decreasing order by effect size, with smaller effects modestly shifts the risk but also increases the confidence intervals. The best risk prediction model should not include the small effect SNPs. The newly proposed screening is superior to the traditional screening strategy as evaluated by net benefit quantity. 2) Many methods have been developed for SNP selection, SNP effect estimation, and risk prediction. A Bayesian method designed for continuous phenotypes, BayesR, shows good characteristics. Here, I developed an extension of BayesR (BayesRB), so that the method can be used for binary phenotypes. I evaluated the performance of BayesRB. It performs well on SNP effect estimation and risk prediction, but not on associated SNP selection. 3) Recessive forward genetic screening study (RFGSS) is widely conducted for disease mutation detection. Estimating the screening saturation in a RFGSS guides the screening strategy. Here, I developed a simulation-based "unseen species" method to estimate the screening saturation in a RFGSS. I simulated a RFGSS process based on a real study and compared my method to both non-parametric methods and parametric methods. The proposed method preforms better than all the other methods, except an existing "unseen species" method. The above three newly proposed methods help better construct risk prediction models and estimate the number of disease contributing genes. These methods can be applied to different disease studies and will improve the knowledge of the diseases and make a positive contribution to disease treatment and prevention.

Last Updated On Monday, September 12, 2016 by Valenti, Renee Nerozzi
Created On Tuesday, May 31, 2016

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