Perelman School of Medicine Events Calendar

New staff orientation is a half-day program that provides an overview of the SOM as well as specific useful information about key functions and resources that are important to SOM staff. Specifically, the program will help new staff:

• feel welcome
• understand the School of Medicine's past, mission, and vision for the future
• understand the School of Medicine's organizational and reporting structure
• learn relevant functional departments contacts and numbers
• identify resources that are available to support you as a School of Medicine employee

It is expected that all new employees and transfers to the School of Medicine will participate in the orientation as soon after their start date as possible. Orientation sessions are generally scheduled monthly, but may vary with the volume of new hires. Invitations are sent directly to all new staff and transfers at their home address. There are three ways to register:

• Call our office at 215-573-0682
• Email to oe@mail.med.upenn.edu
• Register on Knowledge Link (Course title: New Staff Orientation SOM)

David Hill Thesis Defense

Mentor: David Artis

Time: 1pm

Location: Class of '62, JMB

Tishina Okegbe Thesis Defense

Mentor: Steven DiNardo

Time: 12pm

Location: BRB Auditorium

Craig Wilen Thesis Defense

Mentor: Robert Doms

Time: 2pm

Location: Austrian Auditorium

A counselor from Career Services will hold walk-ins, every other Thursday for Biomedical Postdocs from 10:00am to 12:00pm. Please bring your Penn ID so they can confirm your BPP postdoc status.
Services from a counselor include:Critiques of c.v.’s, resumes, cover letter and other job hunting materials, advice about conducting an effective job search, preparation for interviews, assistance with defining your career direction.

A counselor from Career Services will hold walk-ins, every other Thursday for Biomedical Postdocs from 10:00am to 12:00pm. Please bring your Penn ID so they can confirm your BPP postdoc status.
Services from a counselor include:Critiques of c.v.’s, resumes, cover letter and other job hunting materials, advice about conducting an effective job search, preparation for interviews, assistance with defining your career direction.

Stellar Chance 104 

Please come to learn more about the program as well as information about the council . They will be covering a broad range of information to give you a better idea of necessary things to know during your time here at the University of Pennsylvania. No registration is neccessary. Please bring any questions you have with you.

John Morgan Building,  Class of '62 Auditorium 

Topic: Peer Review

Registration is required to attend this session. No one will be permitted to attend the session that did not register.
In addition, due to the nature of the session, anyone arriving more than 10 minutes late will not receive credit for attending the session.

CAMB Thesis Defense: Natalie Wolkow

Mentor: Joshua Dunaief

Time: 10am

Location: Barchi Library

James Findley, PhD "CBT for Insomnia"

“Maximum Likelihood Estimation for Longitudinal Binary Data with Time-Dependent Covariates”

Matthew Guerra

Ph.D. Candidate

Division of Biostatistics

Department of Biostatistics and Epidemiology

Dissertation Advisor: Justine Shults, PhD
Dissertation Committee Chair: Phyllis Gimotty, PhD
Dissertation Committee Members: J. Richard Landis, PhD, Sarah Ratcliffe, PhD, DuPont Guerry, IV, MD

Abstract: We consider longitudinal studies with binary outcomes that are measured repeatedly on subjects over time. Our analysis goal is to fit a logistic model that relates the expected value of the outcomes with explanatory variables that are measured on each subject. However, additional care must be taken to adjust for the association between the repeated measurements on each subject.

In this dissertation, we propose the new first-order Markov maximum likelihood (MARK1ML) method for covariates that may be fixed or time-varying. We also implement and make comparisons with two other approaches: generalized estimating equations, which may be more robust to misspecification of the true correlation structure, and alternating logistic regression, which models association via odds-ratios, that are subject to less-restrictive constraints than are correlations. We prove that the proposed estimation procedure will yield consistent and asymptotically normal estimates of the regression and correlation parameters, if the correlation on consecutive measurements on a subject is correctly specified. Simulations (conducted with a new and simple approach that we present in this dissertation) demonstrate that our approach can yield improved efficiency in estimation of the regression parameter; for equally spaced and complete data, the gains in efficiency were greatest for the parameter associated with a time by group interaction term and for higher values of the correlation. For unequally spaced data and with drop-out according to a missing at random mechanism, MARK1ML with correctly specified consecutive correlations yielded substantial improvements both in terms of bias and efficiency. We present analyzes from longitudinal studies in psychiatry and cancer prevention to demonstrate application of the methods we consider. We also offer an R function for easy implementation of our approach.

“Wrestling with Issues in Scale Development Using Joint Latent Variable Methods”

Steffanie M. Halberstadt

Ph.D Candidate

Division of Biostatistics

Department of Biostatistics and Epidemiology

Dissertation Advisor: Mary Sammel, ScD
Dissertation Committee Chair: Knashawn Morales, PhD
Dissertation Committee Members: Andrea Troxel, ScD, Kathryn Schmitz, PhD, MPH, Ellen W. Freeman, PhD

Abstract: In this dissertation I explore the use of joint latent variable methods for the development of summary scales used in clinical studies. The primary aims of scale development are to combine relevant pieces of information necessary to describe an underlying hypothetical construct (item selection) and to demonstrate that the scale measures the construct appropriately by comparing the scale to other measures of the same construct (validation). Joint latent variable methods are a natural choice for scale development because they model the relationship between multiple scale items and latent constructs simultaneously and provide a measure of the association between the latent construct and a "gold standard" validation measure. Combining the two stages of item selection and validation into a single model, joint latent variable models eliminate the bias that is inherent in modeling these processes separately. Motivating the methodology in this dissertation is an example from the Physical Activity and Lymphedema (PAL) clinical trial. What constitutes a "gold standard" clinical diagnostic measure is a controversial issue in lymphedema research. Many objective diagnostic measures are expensive, time-consuming, and fail to provide a comprehensive measure of all important attributes of lymphedema. Patient-reported symptoms have the potential to be a useful indicator of lymphedema because they an inexpensive and quick assessment of acute changes in swelling, skin tone, and function. Our objective was to simultaneously identify important patient-reported lymphedema symptoms and to compare these symptoms to a current clinical diagnostic measure. However, a unique feature of the PAL sample was the presence of significant symptom non-response. To account for this, I propose a multivariate zero-inflated proportional odds (MZIPO) model, a joint latent variable model that combines continuous and categorical latent variables to perform item selection and validation in the presence of zero-inflation in scale item distributions. This new model classifies subjects into a susceptible class, which includes those subjects who are prone to experiencing lymphedema symptoms, and an unsusceptible class, which includes subjects who are truly invulnerable to suffering from symptoms. For the susceptible class, the model provides estimates of correlation between individual symptoms and a latent measure of lymphedema severity as well as an association between the clinical diagnostic measure and lymphedema severity. In addition to determining the value of patient-reported symptoms in comparison to "gold standard" diagnostic measures, the MZIPO model is advocated for its ability to identify a significant unobserved subgroup of patients who may require careful monitoring for lymphedema exacerbations or flare-ups. 

Just testing it all out

Location: Unknown

Addiction Module

Teresa R. Franklin, Ph.D.
Assistant Professor of Neuroscience in Psychiatry
Treatment Research Center
Perelman School of Medicine, University of Pennsylvania

“Personalizing Therapies for Smokers Wanting to Quit”

Location: BRB II/III Auditorium