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

Di Zhang - Inference on Win Ratio for Clustered Semi-Competing Risk Data

Thursday 4/11 10:00AM - 12:00PM
1149 Public Health, Foster Conference Room

Di Zhang of the Department of Biostatistics defends her dissertation on “Inference on Win Ratio for Clustered Semi-Competing Risk Data”.

Committee Chairperson: Jong H. Jeong, PhD, Department of Biostatistics

Committee Members:

Ying Ding, PhD, Department of Biostatistics

Chaeryon Kang, PhD, Department of Biostatistics

Stephen R. Wisniewski, PhD, Department of Epidemiology

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


ABSTRACT:

 

Composite endpoints are commonly used in public health with an anticipation that clinically relevant endpoints as a whole would yield meaningful treatment benefits. The traditional way to analyze composite endpoints weights each endpoint equally. This can lead to difficulties in interpreting study results when the components have different clinical importance. The win ratio statistic was proposed recently to resolve this issue by prioritizing the important endpoints through sequential comparisons. The statistical method developments for the win ratio were only in randomized controlled trial settings with independent subjects and no potential confounders. Considering the increasing popularities of research using real-world evidence, we developed statistical frameworks of the win ratio in cluster randomized trial and observational study settings. Throughout the dissertation, we focus on composite endpoints of semi-competing risk structure and two comparison arms, though the proposed techniques could be extended to other types of composite endpoints and multiple comparison arms.

Firstly, we propose to model the win ratio of cluster-randomized data non-parametrically using bivariate clustered U-statistics. The proposed method accounts for the potential dependence among subjects within the same cluster. The asymptotic joint distribution of the joint clustered U-statistics is derived. The asymptotic variance and covariance estimators are constructed and evaluated. Several simulation studies are conducted to assess the type I error probabilities and powers of the test statistic. Then the proposed method is illustrated using a multi-center breast cancer clinical trial.

Secondly, the causal inference for the win ratio in observational studies with independent subjects is developed. We propose to use a combination of propensity score analysis with inverse probability weights and U-statistics. The causal estimand of the proposed estimator is average superiority effect, which is based on the average over marginal distributions of potential outcomes for comparison groups. The asymptotic properties of the proposed test statistic are studied. The asymptotic variance is derived and evaluated through simulation studies.

Lastly, based on the causal inference procedure developed in the second part, we propose a weighted stratified win ratio estimator based on calibrated weights for cluster-correlated data from observational studies. The calibration technique used in the weight estimation creates a good balance of covariates and cluster effects between arms in the overall sample. Additionally, it is robust against misspecified distributional assumptions. The asymptotic properties of the proposed estimator are derived, and the finite sample performance of the estimator is evaluated through simulation studies. The proposed method is applied to an observational study on children with traumatic brain injury (ADAPT trial), using sites or regions as clusters.

Our work has important implications to public health, providing new analytical tools to assess the intervention benefits using informative endpoints, to promote public health and transform health care.

Last Updated On Monday, March 25, 2019 by Valenti, Renee Nerozzi
Created On Monday, March 25, 2019

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