Biostatistics Events

Biostatistics Departmental Calendar

Event
Thu 2/27/2020
Biostatistics Event
Biostatistics Research Day 2020 Biostatistics Event
Biostatistics Research Day 2020
Thu 2/27/2020
1155 Public Health, Foster Conference Room

Biostatistics Research Day is an annual departmental event that showcases student research and promotes interdisciplinary research among graduate students and faculty in Public Health.
This year's speaker is James Dignam, PhD (BIOST '94).


1155 Public Health, Foster Conference Room
Thu 2/27/2020 3:30PM - 4:30PM
Biostatistics Seminar Series
Big Clinical Trials: The NCI National Clinical Trials Network - James Dignam, University of Chicago Biostatistics Seminar Series
Big Clinical Trials: The NCI National Clinical Trials Network - James Dignam, University of Chicago
Thu 2/27/2020 3:30PM - 4:30PM
Public Health Lecture Hall (A115)


Public Health Lecture Hall (A115)
Thu 3/5/2020 3:30PM - 4:30PM
Biostatistics Seminar Series
Lin Chen, University of Chicago Biostatistics Seminar Series
Lin Chen, University of Chicago
Thu 3/5/2020 3:30PM - 4:30PM
Public Health Lecture Hall (A115)


Public Health Lecture Hall (A115)
Thu 3/19/2020 3:30PM - 4:30PM
Biostatistics Seminar Series
Dipankar Bandyopadhyay, Virginia Commonwealth University Biostatistics Seminar Series
Dipankar Bandyopadhyay, Virginia Commonwealth University
Thu 3/19/2020 3:30PM - 4:30PM
Public Health Lecture Hall (A115)


Public Health Lecture Hall (A115)
Sun 3/22/2020 to Wed 3/25/2020
Biostatistics Conference
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.


Mon 3/23/2020 7:00AM - 8:30AM
Biostatistics Event
Pitt Biostatistics Breakfast at ENAR Biostatistics Event
Pitt Biostatistics Breakfast at ENAR
Mon 3/23/2020 7:00AM - 8:30AM



Thu 4/9/2020 3:30PM - 4:30PM
Biostatistics Seminar Series
Heping Zhang, Yale University Biostatistics Seminar Series
Heping Zhang, Yale University
Thu 4/9/2020 3:30PM - 4:30PM
Public Health Lecture Hall (A115)


Public Health Lecture Hall (A115)
Thu 4/16/2020 3:30PM - 4:30PM
Biostatistics Seminar Series
Jeffrey S. Morris, University of Pennsylvania Biostatistics Seminar Series
Jeffrey S. Morris, University of Pennsylvania
Thu 4/16/2020 3:30PM - 4:30PM
Public Health Lecture Hall (A115)


Public Health Lecture Hall (A115)
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

Xingyuan Li - Modeling Exposure-Time-Response Association in the Presence of Competing Risks

Friday 1/18 1:00PM - 3:00PM
7139 Public Health, Peterson Seminar Room

Xingyuan Li of the Department of Biostatistics defends her dissertation on "Modeling Exposure-Time-Response Association in the Presence of Competing Risks". 

Committee Chairperson: Joyce Chang, PhD, Department of Medicine

Committee Members: 
Stewart Anderson, PhD, Department of Biostatistics
Yu Cheng, PhD, Department of Statistics
Julie M. Donohue, PhD, Department of Health Policy Management 
Robert Krafty, PhD, Department of Biostatistics 
 

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


ABSTRACT:

In longitudinal pharmacoepidemiology studies, the exposures may be chronic over a period of time and the intensity, duration, and timing of the exposures may vary among subjects. Further challenges may arise when the data involve competing risks, where subjects may fail from one of multiple events and failure from one precludes the risk of experiencing others.

A model that predicts the risk of a health outcome from the longitudinal pattern of exposures can help the researchers and health care professionals identify high-risk individuals more efficiently. However, methodological challenges arise in at least three aspects: 1) how to account for the time-varying nature of exposures?  2) is there is a cumulative and latency effect such that exposures could contribute to the risk of a health outcome incrementally over time? and 3) is there any competing event that precludes the outcome of interest?

The dissertation focuses on how to overcome these challenges in the development of methods for directly modeling the probability of the main event of interest (aka, cumulative incidence function, CIF). In Section 1 of the dissertation, we propose a subdistribution hazards regression model. The model incorporated weighted cumulative effects of the exposure so the intensity, duration, and the timing of the exposure can be taken into consideration simultaneously. We incorporated penalized cubic B-splines into the partial likelihood equation to estimate the weights. Performance of the proposed model was evaluated through a simulation study.

In Section 2 of the dissertation, we extend the model in Section 1 to a more general form and propose a generalized transformation regression model. In Section 1, the subdistribution hazard ratio was modeled as a function of time lag between exposure initiation and risk estimation. In this section, we allowed the subdistribution hazard ratio to be a bivariate function of both the time lag and the level of time-varying exposures. We also introduced various link functions to model the CIF, such that the method in Section 1 can be considered as a special case of this general definition. We used tensor product splines with penalties to flexibly estimate the bi-dimensional surface for the cumulative effects of exposure, and incorporated an additional ridge penalty to constrain the model behavior at the right tail of lag dimension. Extensive simulations were conducted to evaluate the model performance.

To illustrate our proposed method, we applied the models to investigate the association between patterns of prescription opioid use and the risk of overdose (and other adverse outcomes) for Medicare and Medicaid beneficiaries, treating mortality as a competing risk.

PUBLIC HEALTH SIGNIFICANCE: We introduced novel statistical methods that directly estimate the cumulative effect of time-dependent exposures, to quantify the exposure-time-response association in which the intensity, duration, and timing of an exposure are taken into consideration while the event of interest is subject to competing risks. Using opioid use in Medicare and Medicaid as examples, we showed that the proposed model is able to distinguish different prescription patterns even though these patterns have the same overall intensities during the study period, which allows clinicians and health policy practitioners to better understand the long-term effects of opioid use, identify high-risk individuals for overdose (and other adverse outcomes), and guide decisions that may mitigate the prescription opioid epidemic in the United States. The method is also generalizable to other pharmacoepidemiological studies regarding prescription medication use.

 

Last Updated On Monday, September 23, 2019 by Valenti, Renee Nerozzi
Created On Friday, January 4, 2019

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