School of Medicine Calendar - ALL
All School of Medicine events for staff (does not include Student events) - ( Details and Feeds
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The Institute for Diabetes, Obesity, and Metabolism; the Division of Endocrinology, Diabetes, and Metabolism; and the Diabetes Research Center
Combined Fall 2012 Seminar Series presents:
Krishna Chatterjee, FMedSci
University of Cambridge
“Nuclear Hormone Synthesis and Action: Insights from Human Disorders”
September 24, 2012
12:00 PM
Translational Research Center (TRC) Auditorium
Richard K. Assoian, Ph.D.
Department of Pharmacology
Perelman School of Medicine
Title: Microenvironment stiffness and intracellular force in cell cycling and cardiovascular disease
For additional information: (215) 573-9758
University of Cambridge
Nuclear Hormone Synthesis and Action: Insights from Human Disorders
*co-sponsored by ITMAT and IDOM
When: Monday, September 24th
If you're serious about maximizing your career options, you need to network. But what is networking, and how can it help you find the right job? Career Services is hosting a workshop next Monday that will cover all the basics of networking and help you build a network that can launch your career and move it forward.
To learn more about networking, go to:
http://chronicle.com/article/How-Do-I-Create-a-Professional/128232/
The workshop will start promptly at noon, but seating is limited, so plan to come early!
John Tuton, PhD
Career Services
University of Pennsylvania
Philadelphia, PA 19104
215-898-7530
jftuton@upenn.edu
“Neural Injury in Obstructive Sleep Apnea”
"Statistical Methods for Evaluating Diagnostic Biomarkers in the Presence of Measurement Error"
Matthew T. White
PhD Candidate
Division of Biostatistics
Department of Biostatistics and Epidemiology
Dissertation Advisor: Sharon X. Xie, PhD
Committee Chair: Kathleen J. Propert, ScD
Committee Members: Justine Shults, PhD, Daniel Weintraub, MD
Abstract: In recent years, biomarkers have grown in importance in many clinical and epidemiological settings. Many biomarkers are obtained with measurement error due to imperfect lab conditions or temporal variability within subjects, and it is therefore critical to develop analytical methods to quantify and adjust for measurement error in the evaluation of diagnostic markers.
We first develop a parametric bias-correction approach to adjust estimates of sensitivity, specificity, and other diagnostic measures for measurement error by using an internal reliability sample. We derive asymptotic expressions for the bias in naive estimators. We prove that the bias-corrected estimators are consistent and asymptotically normally distributed and derive the asymptotic variance of the estimators using the delta method. We evaluate our method through extensive simulations and illustrate our method using a biomarker study in Alzheimer's disease (AD).
Next, we develop optimal design strategies for studying the effectiveness of an error-prone biomarker in differentiating diseased from non-diseased individuals and focus on the area under the receiver operating characteristic curve (AUC) as the primary measure of effectiveness. Using an internal reliability sample within the diseased and non-diseased groups, we develop optimal study design strategies that 1) minimize the variance of the estimated AUC subject to constraints on the total number of observations or total cost of the study or 2) achieve a pre-specified power. We develop optimal allocations of the number of subjects in each group, the size of the reliability sample in each group, and the number of replicate observations per subject in the reliability sample in each group under a variety of commonly seen study conditions.
Finally, we propose a parametric approach to compare two or more correlated AUCs when the biomarkers are subject to correlated measurement errors. We show that the proposed estimator is consistent and asymptotically normally distributed and derive its asymptotic variance using the delta method. We compare the performance of our method to naive methods that ignore the correlation in measurement errors through simulations and show that ignoring this correlation can lead to biased estimates of the AUC difference. We return to the AD biomarker study to demonstrate our method.