Department of Radiation OncologyRadiation Biology Research DivisionAll Division Meeting 12am-1pm in Room 8-146A; Lunch will be served at 11:45am
Seth Himelhoch, MD, "Hepatitis C Virus - Risk Assessment and Treatment Considerations Among People with Serious Mental Illness"
Department of Radiation Oncology
Division of Radiation Biology Research Seminar Series
Timothy Chan, MD PhD
Director of Translational Oncology Division
Department of Radiation Oncology
Memorial Sloan Kettering Cancer Center
12pm-1pm in Room 8-146AB;
Lunch will be served at 11:45am
PennChart Research Workflow - OCR
This course includes enrolling subjects in a research study, maintaining the enrollment record, attaching the research guarantor to the subject's registration and scheduling visits. Staff will also learn how to access, navigate and review the EMR (Electronic Medical Record). Included in this class are: placing research orders, adding clinical documentation, entering and associating orders, entering new encounters and working with the InBasket functionality. Upon completion of this course you will have the ability to place and pend research orders. Lunch is not provided.
Time: 9:00am - 3:00pm
Location: 2nd floor of Founders Cafeteria; computer lab is located in the back of the dining hall door on the left.
“ACTIVATABLE FLUOROPHORES FOR THE MOLECULAR IMAGING OF CYTOSOLIC PHOSPHOLIPASE A2 IN BREAST CANCER”
This workshop will take place in a computer lab. Participants will engage in hands-on activities in Microsoft Excel 2007 that promote the following skills: formatting, conditional formatting, drop down lists, data validation and alerts, some formulas, VLOOKUPs and workbook tracking log. It is recommended that you bring a memory stick to save your work for future reference. Registration is limited due to room capacity. Please note that this workshop is designed for participants who have completed the Essential Computer Skills for the CRC - Excel I course or have a solid understanding and comfort level with Excel. Credit hours from this course can be counted towards your Penn CRC Certification training.
Please note that this workshop is designed for participants who have completed the Essential Computer Skills for the CRC - Excel I course, or have a solid understanding and comfort level with Excel.
"Sensitivity analysis for non-ignorable dropout of marginal treatment effect in longitudinal trials for G-computation based estimators"
Emin Tahirovic, MS PhD Candidate Division of Biostatistics Graduate Group in Epidemiology and Biostatistics
Abstract: Estimators that adjust for non-ignorable dropout in a longitudinal clinical trial can be roughly classified in two ways: those that model the probability of dropout explicitly and those that adjust for dropout by specifying a model for the outcome. We discuss the intricacies related to the potential overlap of identification and specification in the latter class. We specify the assumption under which dropout is ignorable w.r.t. two estimators (linear increments (LI) and extended SWEEP) from this class. Further, we present a sensitivity analysis approach w.r.t. this baseline assumption in a longitudinal trial that allows more intuitive and informed input from domain experts about a simple sensitivity parameter. We show that the unconditional treatment-specific mean is identified under the assumption of future independence given present. We apply our sensitivity analysis approach in a dataset coming from a multi-center cluster-randomized controlled trial comparing two alternative economic interventions for reducing LDL cholesterol among patients with high cardiovascular risk. In order to position our approach among existing sensitivity analysis tools, we illustrate how it can be viewed as an extension of Daniels and Hogan’s (2007) pattern-mixture approach to longer sequences of observations.
Dissertation Advisor: Andrea B. Troxel, ScD Committee Chair: Sharon X. Xie, PhD Committee: Kevin G. Lynch, PhD, Kevin G. Volpp, MD, PhD
“Ignorability Conditions for Incomplete Data and the First-Order Markov Conditional Linear Expectation Approach for Analysis of Longitudinal Discrete Data with Overdispersion”
Shaun Bender, MS PhD Candidate Division of Biostatistics Graduate Group in Epidemiology and Biostatistics
Abstract: Medical researchers strive to collect complete information, but most studies will have some degree of missing data. We first study the situations in which we can relax well accepted conditions under which inferences that ignore missing data are valid. We partition a set of data into outcome, conditioning, and latent variables, all of which potentially affect the probability of a missing response. We describe sufficient conditions under which a complete-case estimate of the conditional cumulative distribution function of the outcome given the conditioning variable is unbiased. We use simulations on a renal transplant data set to illustrate the implications of these results. After describing when missing data can be ignored, we provide a likelihood based statistical approach that accounts for missing data in longitudinal studies, by fitting correlation structures that are plausible for measurements that may be unbalanced and unequally spaced in time. Our approach can be viewed as an extension of generalized linear models for longitudinal data that is in contrast to the generalized estimating equation (GEE) approach that is semi-parametric. Key assumptions of our method include first-order antedependence within subjects; independence between subjects; exponential family distributions for the first outcome on each subject and for the subsequent conditional distributions; and linearity of the expectations of the conditional distributions. Our approach is appropriate for data with over-dispersion, which occurs when the variance is inflated relative to the assumed distribution. We first consider a clinical trial to compare two treatments for seizures in patients. We implement the Poisson and Negative Binomial distributions for analysis of the seizure counts and perform a likelihood ratio test to choose between the two distributions. Next, we consider a study that evaluates the likelihood that a transplant center is flagged for poor performance. The outcome variable is a binomial type outcome that indicates the number of times the center was flagged in the previous time-period. For both studies, we perform simulations to assess the properties of our estimators and to compare our approach with GEE. We demonstrate that our method outperforms GEE, especially as the degree of over-dispersion increases. We also provide software in R so that the interested reader can implement our method in his or her own analysis.
“Novel Statistical Methodologies in Analysis of Position Emission Tomography Data: Applications in Segmentation, Normalization, and Trajectory Modeling”
Daniel Shin, MS PhD Candidate Division of Biostatistics Graduate Group in Epidemiology and Biostatistics
Abstract: Position emission tomography (PET) is a powerful functional imaging modality with wide uses in fields such as oncology, cardiology, and neurology.Motivated by imaging datasets from a psoriasis clinical trial and a cohort of Alzheimer's disease (AD) patients, several interesting methodological challenges were identified in various steps of quantitative analysis of PET data.In Chapter 1, we consider a classification scenario of bivariate thresholding of a predictor using an upper and lower cutpoints, as motivated by an image segmentation problem of the skin.We introduce a generalization of ROC analysis and the concept of the parameter path in ROC space of a classifier.Using this framework, we define the optimal ROC (OROC) to identify and assess performance of optimal classifiers, and describe a novel nonparametric estimation of OROC which simultaneous estimates the parameter path of the optimal classifier.In simulations, we compare its performance to alternative methods of OROC estimation.In Chapter 2, we develop a novel method to normalize PET images as an essential preprocessing step for quantitative analysis.We propose a method based on application of functional data analysis to image intensity distribution functions, assuming that that individual image density functions are variations from a template density. By modeling the warping functions using a modified function-on-scalar regression, the variations in density functions due to nuisance parameters are estimated and subsequently removed for normalization.Application to our motivating data indicate persistence of residual variations in standardized image densities.In Chapter 3, we propose a nonlinear mixed effects framework to model amyloid-beta (Aβ), an important biomarker in AD.We incorporate the hypothesized functional form of Aβ trajectory by assuming a common trajectory model for all subjects with variations in the location parameter, and a mixture distribution for the random effects of the location parameter address our empirical findings that some subjects may not accumulate Aβ. Using a Bayesian hierarchical model, group differences are specified into the trajectory parameters. We show in simulation studies that the model closely estimates the true parameters under various scenarios, and accurately estimates group differences in the age of onset.
Dissertation Advisor: Russell T. Shinohara, PhD Committee Chair: Andrea B. Troxel, ScD Committee: Haochang Shou PhD, Joel M. Gelfand, MD, MSCE, Nehal N. Mehta, MD, MSCE
CHOP and Penn are coming together to reinvigorate the University City Chapter for SOCRA. To kick things off we have the following workshop at 9am on July 15th 2016 at the CRB auditorium- see details below. Please share this announcement with your colleagues.
This presentation aims to provide an overview of resources for research study teams:
- Q&A offers opportunity for study coordinators and managers to discuss what they want/need to do their jobs and what additional resources would be beneficial.
- A panel of coordinators (nurses. IND/IDE, industry studies, CHOP/Penn collaborations, etc) will be available during the Q&A period.
ABOUT THE SPEAKERS:
Katherine Yang-Iott is a Research Navigator with The Children’s Hospital of Philadelphia (CHOP) Clinical research Support Office (CRSO). Maha Dutt is a Senior Clinical Research Operations Specialist with the University of Pennsylvania Perelman School of Medicine (PSOM) Office of Clinical Research (OCR).
Program is FREE and is open to both SOCRA & Non-SOCRA members. Registration is not required.
SoCRA members may claim up to 1.5 SOCRA CEU for SOCRA recertification. SOCRA members will receive a certificate of attendance.
Contact Kristi Lelionis, MS, CCRP, Chapter Chair at email@example.com or Jenna Tress, MS, CCRP at firstname.lastname@example.org .
This meeting will be held in the Austrian Auditorium, Clinical Research Building, the University of Pennsylvania, 415 Curie Boulevard, Philadelphia, PA 19104. View Campus Map - type in "Clinical Research Building" in search box.
HUP is a 5-10 minute walk from the University City station (SEPTA Regional Rail). To map your trip or to find out about possible delays, visit the SEPTA site.
SEPTA Regional Rail: The Airport, Marcus Hook-Wilmington, Media-Elwyn and most Glenside-Jenkintown, Warminster and West Trenton trains provide direct service to the University City Station. From other Regional Rail lines, transfer free to the Airport, Marcus Hook-Wilmington, Media-Elwyn and most Glenside-Jenkintown, Warminster and West Trenton trains at 30th Street or Market East stations. Your train ticket is your transfer.
SEPTA City Trains and Trolleys: The SEPTA Market/Frankford Blue Line (the elevated train/subway) stops at 34th and Market Sts. The CRB is a 10-15 minute walk from there. All SEPTA Subway/Surface Green Line trolleys except the Number 10 stop at 37th and Spruce streets; it is less than a 10 minute walk to the CRB.
Loop Through University City (LUCY): The LUCY Gold Loop takes passengers from 30th Street Station and 34th and Market Streets to Curie Blvd (in front of the BRB) and then onto Penn Presbyterian Medical Center. The Gold Loop shuttle runs every 10 minutes during peak times (7 a.m. to 9 a.m. and 4 p.m. to 6 p.m.) and every 30 minutes from 9 a.m. to 4 p.m. LUCY is $2.00 or a SEPTA token for the public and is free with a SEPTA pass or ID from the University of Pennsylvania, University of Pennsylvania Health System, Drexel University, Children's Hospital of Philadelphia and the VA Medical Center.
Visit Google Maps for driving directions or View Penn Medicine Driving Directions
**Charges apply** and are the responsibility of the attendee. Please note that the available parking garages are several blocks from HUP.
Self -parking is available at Lot 7 for a flat rate of $20 per day. Lot 7 is located on Convention Avenue across from the University City train station. View Penn Parking Map
Valet parking is available at HUP and the Perelman Center for Advanced Medicine. Parking at both the Perelman Center and HUP are primarily reserved for patients and their families, with limited parking for campus visitors.
Please share this announcement with your colleagues.
Department of Radiation Oncology
Radiation Biology Research Division
12am-1pm in STCR 8-120 (Oval conference room)
Seminar title: Unraveling the transcriptional and metabolic oncogenic programs controlled by NOTCH1 in T-ALL
Tuesday, August 2nd 2016
12:00 pm - 1:00 pm
8030 Maloney Building
If you have not had a chance to attend one of our previous meetings; this is open to all Clinical Research Coordinators, Project Managers, Directors and the like, to foster your professional development and network with colleagues.