Perelman School of Medicine Events Calendar

Democracy, Governance and Curbing Corruption in Sub-Saharan Africa

Location: Cheyney University of Pennsylvania

An international conference organized by the Master of Public Administration Program, in collaboration with The African & Caribbean Business Council.

While other regions of the world are making strides in improving the living standards of their citizens, Sub-Saharan Africa (SSA) is still trapped in poverty with more than 40% of its 600 million people living below the internationally recognised absolute poverty level of US$ 1 per day. Several reports highlight corruption as one of the institutional and systemic problems responsible for keeping million of Africans under absolute poverty. In fact, the 2008 Transparency International report demonstrates a direct link between corruption and failure of societal institutions to effectively achieve the Millennium Development Goals (MDGs) in SSA. It is a serious concern that the goals of halving poverty in SSA by the year 2015 from the level of 1990 will not be met.

For additional information including call for papers, registration and lodging, please visit the conference website at: http://www.cheyney.edu/international-anticorruption/index.cfm

Katie Bar, M.D.
"Viral Dynamics of HIV-1 Rebound Viremia"

Haim Bau, Ph.D. and Changchun Liu, Ph.D.
”Point of Care, Integrated System for Molecular Detection of HIV”

252 BRB

The Pharmacology Graduate Group is pleased to announce the

Join us for
the ICAP Nursing Curriculum Development Webinar Series
Part 2  

Presented by:

Jennifer Dohrn, DNP, CNM, Project Director, ICAP Global Nurse Capacity Building Program

Marita Murrman, EdD, MS, RN, CHES, Technical Consultant for Training and Education at ICAP and Associate Professor in Sociomedical Sciences at the Columbia Mailman School of Public Health   

& 

Janel Smith, MPH, RN, Curriculum Design Specialist, ICAP Global Nurse Capacity Building Program

Using Task Analysis to Harmonize  

Nursing and Midwifery Training  

&  

the Reality of the Work Environment in  

Lesotho and Botswana

 Guest Speakers:

  Peter Johnson, CNM, PhD, Director of Global Learning, Jhpiego

&

Leah Hart, MSN, MPH, Technical Development Officer, Jhpiego

Thursday, July 11, 2013 at 9:00 am EST

(1:00 pm Cote D'Ivoire; 3:00 pm Johannesburg; 4:00 pm Nairobi)

 Click to join the webinar 

Session Description: 

It is important for health care professional graduates to be able to competently provide expected and needed services on graduation and deployment. Gaps exist between preparation of students and the realities of practice in the work environment. Pre-service education programs, scope and standards of practice, and regulatory systems must be able to adjust as national health care priorities shift and new diseases and technology emerge. Task analysis is a way to understand and evaluate the actual work of health care providers in the field. Task analysis has been used as a basis for curriculum updates, evaluating accrediting exams and re-defining scopes of practice. Explore task analysis in an online discussion about what it is, how it can be used and how and why it is relevant to curriculum development.

Login Instructions and Tips

 Please enter your name, organization, and location (For example, John Smith-ICAP-New York). For technical instructions on how to use Adobe Connect webinar software, view our  Webinar Tips.

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Michael Perlis, PhD "Why EEG for State Discrimination"
& "The Nuts & Bolts of Sleep Scoring"  RSVP required 

"Issues in Group Sequential/Adaptive Designs"

Hong Wan
PhD Candidate
Division of Biostatistics
University of Pennsylvania Perelman School of Medicine

Abstract: In recent years, there has been great interest in the use of adaptive features in clinical trials (i.e., changes in design or analyses guided by examination of the accumulated data at an interim point in the trial) that may make the studies more efficient (e.g., shorter duration, fewer patients). Many statistical methods have been developed to maintain the validity of study results when adaptive designs are used (e.g., control of the type I error rate). Group sequential designs, which allow early stopping for efficacy in light of compelling evidence of benefit or early stopping for futility when the likelihood of success is low at interim analyses, have been widely used for many years. In this dissertation, we study several aspects of statistical issues in group sequential/adaptive designs. Sample size re-estimation has drawn a great deal of interest due to its permitting revision of the target treatment difference based on the unblinded interim analysis results from an ongoing trial. A possible risk of unblinded sample size re-estimation is that the exact treatment effect being observed at interim analysis might be back-calculated from the modified sample sized, which might jeopardize the integrity of the trial. In the first project, we propose a pre-specified stepwise two-stage sample size adaptation to lessen the information on treatment effect that would be revealed. We minimize expected sample size among a class of these designs and compare efficiency with the fully optimized two-stage design,optimal two-stage group sequential design and designs based on promising conditional power. In the second project, we define the complete ordering of a group sequential sample space and show that a Wang-Tsiatis boundary family or an exponential spending function family can completely order the sample space. We also propose a simple method to transform a spending function to a completely ordered sample space when using the sequential p-value ordering. This method is also extended to beta-spending functions for p-values to reject the alternative hypothesis. In the third project, we propose a simple approach for controlling the family wise error rate in a group sequential design with multiple testing. We apply sequential p-values at the interim analysis from a group sequential design to the sequentially rejective graphical procedure, which is based on the closure principle. We also use simulations to study the operating characteristics of multiple testing in group sequential designs. We show that in terms of expected sample size, using a group sequential design in multiple hypothesis testing is more efficient than fixed sample size designs in many scenarios.

Dissertation Advisor: Susan Ellenberg, PhD
Committee Chair: Kathleen Propert, ScD
Committee: Keaven Anderson, PhD, David Margolis, MD, PhD, Andrea Troxel, ScD

Subhajit Chakravorty, MD
"Medications Prescribed for Sleep"

Richard J. Schwab, MD
"Overview of Sleep Disorders: Part I"

Radiation Oncology Journal Club

 STCR 8-146AB

"Sparse Semiparametric Nonlinear Models with Applications to Chromatographic Fingerprinting"

Michael Wierzbicki
PhD Candidate
Division of Biostatistics
University of Pennsylvania Perelman School of Medicine

Abstract: Medicinal herbs are comprised of a multitude of compounds and the identification of their active composition is an important area of research. High Performance Liquid Chromatography, a popular technique to detect compounds in herbs, outputs a chromatogram, a curve characterized by spikes corresponding to detected compounds. As the particular set of compounds is unique to each herb and spike locations can be used to identify compounds, chromatograms provide a visual representation, or fingerprint, of herbs. Constructing statistical models for the estimation and comparison of chromatographic fingerprints is difficult due to the sparse, spiky nature of chromatograms. Moreover, across different experimental conditions, the location of spikes can be shifted, preventing the establishment of a standardized fingerprint, direct comparison of curves, and efficient compound identification. Here we describe a sparse semiparametric nonlinear mixed effects modeling framework for the registration and estimation of functional data with sparse structures. Data-driven basis expansion is used to model group-averaged curves while parametric modeling of time warping functions aligns curves. Penalized estimation with the Adaptive Lasso penalty provides a unified criterion for curve registration, model selection, and estimation. Furthermore, the Adaptive Lasso estimators possess attractive sampling properties. The performance of the modeling framework and its role in medicinal herb research are demonstrated through its application to two chromatographic data sets.


Dissertation Advisor: Wensheng Guo, PhD
Committee Chair: Warren Bilker, PhD
Committee: Sarah Ratcliffe, PhD, Bruce Turetsky, MD

Thornton B. Alexander Mason II, MD, PhD, MSCE

Location: BRB 251

Advisor: Dr. Xiaolu Yang