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 Seminar Series

Sensitivity analysis via the proportion of unmeasured confounding -Edward Kennedy, Carnegie Mellon U

Thursday 1/23 3:30PM - 4:30PM
Public Health Lecture Hall (A115)

Sensitivity analysis via the proportion of unmeasured confounding

Edward Kennedy, PhD, Department of Statistics and Data Science, Carnegie Mellon University

In observational studies, identification of causal effects is generally achieved by assuming "no unmeasured confounding," possibly after conditioning on enough covariates. Because this assumption is both strong and untestable, a sensitivity analysis should be performed. Common approaches include modeling the bias directly or varying the propensity scores to probe the effects of a potential unmeasured confounder. In this paper, we take a novel approach whereby the sensitivity parameter is the proportion of unmeasured confounding. We consider different assumptions on the probability of a unit being confounded. In each case, we derive sharp bounds on the average treatment effect as a function of the sensitivity parameter and propose nonparametric estimators that allow flexible covariate adjustment. We also introduce a one-number summary of a study's robustness to the number of confounded units. Finally, we explore finite-sample properties via simulation, and apply the methods to an observational database used to assess the effects of right heart catheterization.

Last Updated On Wednesday, January 15, 2020 by Wang, Jiebiao
Created On Wednesday, December 18, 2019

AprMay 2020Jun
SunMonTueWedThuFriSat
262728293012
3456789
10111213141516
17181920212223
24252627282930
31123456

Submit events and news

Click to enter calendar events or share news and announcements.