Events

SYMPOSIUM ON SPATIAL-TEMPORAL DATA SCIENCE FOR POPULATION HEALTH

Eric Boerwinkle, PhD
Dean of UTHealth Houston School of Public Health

Blake Hanson, PhD
Assistant Professor of UTHealth Houston School of Public Health Epidemiology, Human Genetics & Environmental Sciences

Qian Xiao, PhD
Associate Professor of UTHealth Houston School of Public Health Epidemiology, Human Genetics & Environmental Sciences

Jennifer Deegan, MPAff
Vice President of UTHealth Houston Office of the EVP and Chief Academic Officer of Health Policy and Systems Strategy

Kelly Nelson, PhD
Professor of MD Anderson, Department of Dermatology, Division of Internal Medicine

David Berrigan, PhD
Program Director of National Cancer Institute, Division of Cancer Control and Population Sciences, Behavioral Research Program

Wenjun Li, PhD
Professor of UMass Lowell Zuckerberg College of Health Sciences, Center for Health Statistics and Biostatistics Core, Health Statistics and Geography Lab

Date: Friday, March 1
Time: 9am - 4pm
Location: Cooley Center (7440 Cambridge St, Houston, TX 77054)

For More Information: porsha.vallo@uth.tmc.edu

Link to event: go.uth.edu/CSMAPS2024Symposium

Symposium: The IMPACT OF THE PANDEMIC ON DATA SCIENCE AND (BIO)STATISTICS

Dr. Mi Yan
Senior Data Scientist, Intuit Inc

Dr. Mikyoung Jun
Professor of Mathematics and ConocoPhillips Data Science, University of Houston

Dr. Xiaoying Yu
Associate Professor,
Department of Biostatistics & Data Science, The University of Texas Medical Branch

Dr. Cici Bauer
James W. Rockwell Professorship in Public Health (Preventive Medicine and Epidemiology) and Associate Professor Founding Director, Center for Spatial-Temporal Modeling for Applications in Population Science (CSMAPS) 
Department of Biostatistics and Data Science, UTHealth School of Public Health

Dr. Yongsu Lee
Assistant Professor, 
Department of Biostatistics and Data Science, UTHealth School of Public Health

Dr. Yiping (Bonnie) Dou
Assistant Professor,
Center for Spatial-Temporal Modeling for Applications in Population Science (CSMAPS) UTHealth School of Public Health

Statistical Analysis with Electronic Health Records

Vahed Maroufy, PhD

Department of Biostatistics and Data Science, SPH, University of Texas HSC

Abstract: Electronic health records (EHR), although originally designed for billing and documentation purposes, are valuable resources to provide real-world evidences to evaluate treatment effects and disease risks. They are big data sources with full history record, including diagnosis, medication, procedure, demographic, Lab and clinical event table, which makes them strong choices for longitudinal analysis and prediction. They are, however, not precisely recorded, very noisy and heterogeneous, hence quite challenging to process, clean and prepare for statistical analyses. Common problems with preparing EHRs are: massive noise, intense missing, different variable types (cross-sectional, longitudinal, functional, …), etc. In this presentation, I give a review on the different directions and research projects under going in our department. We particularly use Cerner Health Factor database and Blue Cross Blue Shield (BCBSTX) insurance database.  Cerner includes about 50M patients within 15 years period at around 700 hospitals around USA, and BCBSTX covers over 10M insured patients under 65 years of age in Texas. Specifically, I will discuss the issues and subtlety in data cleaning and processing and share a strategy we are developing for handling these issues. I will also briefly talk about three specific undergoing research projects based on these databases.

HACASA meetings are open to the public!  Please pass on copies of this announcement to colleagues and friends and post on appropriate bulletin boards.                                                     

WHEN and WHERE:        *Tuesday, September 11, 2018* at RAS Building, UT School of Public Health

5:30pm – 6:30pm, Social Hours, Room 102A

6:30pm – 7:30pm, Seminar, Room 102A

RAS Building is the primary building of UT School of Public Health, located at 1200 Pressler Street. For parking information and directions please refer to the UTSPH parking websites, the following have been found helpful:

https://sph.uth.edu/about-us/campus-locations-and-parking-information/  

Thank you to UTHealth and the Department of Biostatistics and Data Science at UTSPH for their generous support of HACASA by allowing us the use of their facilities for our gatherings, and sponsoring FREE PARKING. See an officer for a voucher at the meeting.

To help us cover cost of our weekly snacks, we are asking for small donations ($5 or more). Also, please RSVP so we can bring enough snacks and drinks.  Please RSVP to Kristofer Jennings at HoustonASA@gmail.com.

January 21, 2014
Heidi Spratt, PhD 
Associate Professor    UTMB Bioinformatics Program  The University of Texas Medical Branch

Hepatocellular carcinoma (HCC) is the fifth most common cause of cancer and also one of the deadliest. It has recently shown an upward trend in the number of diagnoses per year. One of the precursors to HCC infection is the Hepatitis C virus (HCV). According to the NIH, HCV is one of the main causes of chronic liver disease in the United States. About one-third of HCV patients will develop cirrhosis of the liver. Of those, about 1-2% annually will develop HCC.  Ultimately, 80% of HCC cases are developed from liver cirrhosis. Hepatitis C causes an estimated ten to twelve thousand deaths each year in the U.S. alone; the virus varies greatly in both its course and disease outcome. Many patients infected with HCV are asymptomatic and have some degree of chronic hepatitis, often associated with some degree of fibrosis of the liver. The prognosis for patients with early-stage fibrosis is frequently good.  At the other end of the spectrum are patients with severe HCV who experience all the classic symptoms of the disease, and who ultimately develop liver cirrhosis. The prognosis for such patients is slim and mortality is usually the result. Little is known about how HCV infection progresses to HCC in patients with advanced fibrosis, so the main goal of this project is to discover biomarkers for the detection of early stage liver cancer. Patients with HCV and HCC co-infection will be compared to HCV only infected patients to develop a biomarker panel for the detection of early stage liver cancer. For the development of such biomarkers, nuclear magnetic resonance metabolomics experiments will be conducted on urine samples from infected patients. Machine learning techniques such as multivariate adaptive regression splines will be used to create a biomarker panel that has the ability to predict HCC infection.

Rice University hosts  StatFest 2013

StatFest 2013 was a one day conference aimed at encouraging undergraduate students from historically under-represented groups to consider careers and graduate studies in the statistical sciences.  The conference is an ongoing initiative of the American Statistical Association through its Committee on Minorities in Statistics.  The conference includes presentations from established professionals, academic leaders, and current graduate students that will help attendees understand the opportunities and routes for success in the field.  Panel forums include information and tips for a rewarding graduate student experience, achieving success as an academic statistician, opportunities in the private and government arenas, among other topics. 

Attendance at the conference was free, though registration was required. Look for next year's StatFest in the Fall. Sponsorship was provided by: Rice University, the National Science Foundation, Abbott Laboratories, and the American Statistical Association.

Congratulations 2013 Science Fair Winners!

Junior: Karina Bertelsmann, "Bingo!...In a Round or Two"; Westbrook Intermediate

Ninth: Christine Castagna, "Efficiencies of Sun Rays Capture"; Academy of Science and Technology

Senior: Grant Zhao, "Waist-to-Height Ratio - A Better Index to Diagnose Obesity & Estimate Body Fat: Part II"; Clements HS

 Dr. José-Miguel Yamal

Division of Biostatistics, The University of Texas

Health Sciences Center School of Public Health

Title: Technologies for the detection of cancer: algorithm development and novel statistical methodology

Abstract: We consider here two technologies proposed as accurate and low-cost alternatives for detection of cervical intraepithelial neoplasia (quantitative cytology and optical spectroscopy) and one technology for detection of breast cancer lesions (elastography). We discuss the development of classification algorithms using these three technologies. (1) Classification using quantitative cytology involves classifying a patient based on measurements on a collection of a random number of their cells, e.g., classifying a macro-level object based on measurements of embedded (micro-level) observations within each object. Classification problems with this hierarchical, nested structure have not received the same statistical understanding as the general classification problem. We present some model-based methodologies that address this structure. (2) Classification using optical spectroscopy involves classifying a patient based on functional data. We trained and tested an algorithm for optical spectroscopy as an adjunct to colposcopy as well as optical spectroscopy not including the colposcopic diagnosis, examining the importance of the probe placement expertise. (3) Classification using axial-shear strain elastograms involves acquiring ultrasound signals before and after a small compression of the breast. We developed a simple algorithm that has the potential to improve the accuracy over using ultrasound images alone. Quantitative cytology, optical spectroscopy, and axial-shear strain elastograms show promise for the detection of pre-cancer and cancer.

Topic: Identifying SNPs associated with Mediators in Genomewide Association Studies:

 Application to Smoking Behavior and Lung Cancer

A mediation model explores the direct and indirect effects between an independent variable and a dependent variable by including other variables (or mediators). Mediation analysis has recently been used to dissect the direct and indirect effects of genetic variants on complex diseases using case-control studies. However, bias could arise in the estimations of the genetic variant-mediator association because the presence or absence of the mediator in the study samples is not sampled following the principles of case-control study design. In this case, the mediation analysis using data from case-control studies might lead to biased estimates of coefficients and indirect effects. In this article, we investigated a multiple-mediation model involving a three-path mediating effect through two mediators using case-control study data. We propose an approach to correct bias in coefficients and provide accurate estimates of the specific indirect effects. Our approach can also be used when the original case-control study is frequency matched on one of the mediators. We conducted simulation studies to investigate the performance of the proposed approach, and showed that it provides more accurate estimates of the indirect effects as well as the percent mediated than standard regressions. We then applied this approach to the multiple-mediation study of the mediating effects of both smoking and chronic obstructive pulmonary disease (COPD) on the association between the CHRNA5-A3 gene locus and lung cancer risk using data from a lung cancer case-control study. The results showed that the genetic variant influences lung cancer risk indirectly through all three different pathways. The percent of genetic association mediated was 18.3% through smoking alone, 30.2% through COPD alone, and 20.6% through the path including both smoking and COPD, and the total genetic variant-lung cancer association explained by the two mediators was 69.1%. 

Title: Quality Improvement at MD Anderson Cancer Center

 The Mammography Wars