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

Judah Abberbock: Usage of Surrogate Endpoints in the Design and Analysis of Clinical Trials

Monday 7/17 2:00PM - 4:00PM
7139 Public Health, Peterson Seminar Room

Judah Abberbock of the Department of Biostatistics defends his dissertation on "Usage of Surrogate Endpoints in the Design and Analysis of Clinical Trials"

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


ABSTRACT:

There has been a shift in the conduct of early-stage breast cancer trials in recent years from long adjuvant trials with overall or disease-free survival as the efficacy endpoint to shorter neoadjuvant trials with pathological complete response (pCR), a binary marker, at time of surgery as the endpoint. The Food and Drug Administration (FDA) currently embraces this transition and deems evidence in pCR improvement sufficient for drug approval on condition that long-term data are collected to eventually show efficacy in survival. Incorporating data on pCR in the design and analysis of such a trial is therefore of public health interest. Here, we propose one method to assess the power and sample size of such a trial with using observed neoadjuvant data and another method to estimate certain causal treatment effects on survival conditional on pCR. In the first part, we propose an exponential mixture model for survival time with parameters for the response rates and an estimated benefit in survival from achieving response. Under a fixed sample size, we obtain the empirical power through simulations from the proposed mixture model. We also propose a more efficient method than the empirical approach by applying an estimated average hazard ratio to the Schoenfeld formula. The performance of our methods is assessed via simulation studies. Data from two neoadjuvant cancer clinical trials are used to illustrate these methods. Second, we propose a method under the principal stratification framework to estimate the causal effect of treatment on a binary outcome, conditional on a post-treatment binary response marker in randomized controlled clinical trials. Specifically, we estimate the treatment effect among those who would achieve response if given the treatment. We are able to identify this causal effect under two assumptions. First, we model the counterfactual probability of achieving response under treatment given baseline clinical markers and the outcome. Second, we assume a monotonicity condition: a patient who responds under control would respond under treatment as well. We compared the performance of proposed method with other standard approaches in simulation studies. Data from a neoadjuvant breast cancer clinical trial are used to demonstrate the proposed method.

Last Updated On Monday, October 23, 2017 by Valenti, Renee Nerozzi
Created On Tuesday, May 30, 2017

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