David Sprott distinguished lecture by Raymond J. Carroll, Texas A&M University

Karen Richardson — Wed, 19 Aug 2015 16:54:06 +0000

David Sprott distinguished lecture by Raymond J. Carroll, Texas A&M University Karen Richardson Wed, 08/19/2015 - 12:54

Constrained maximum likelihood estimation for model calibration using summary-level information from external big data sources.

Information from various public and private data sources of extremely large sample sizes are now increasingly available for research purposes. Statistical methods are needed for utilizing information from such big data sources while analyzing data from individual studies that may collect more detailed information required for addressing specific hypotheses of interest. We consider the problem of building regression models based on individual-level data from an "internal'' study while utilizing summary-level information, such as information on parameters for reduced models, from an "external'' big-data source. We identify a set of constraints that link internal and external models. These constraints are used to develop a framework for semiparametric maximum likelihood inference that allows the distribution of the covariates to be estimated using either the internal sample or an external reference sample. We develop extensions for handling complex stratified sampling designs, such as case-control sampling, for the internal study. Asymptotic theory and variance estimators are developed for each case. We use simulation studies and a real data application to assess the performance of the proposed methods.

This is joint work with Nilanjan Chatterjee (Johns Hopkins), Yi-Hau Chen (Academia Sinica, Taipei) and Paige Maas (National Cancer Institute)

David Sprott distinguished lecture by William Woodall, Virginia Tech

Elizabeth Scott — Tue, 24 Feb 2015 14:15:55 +0000

David Sprott distinguished lecture by William Woodall, Virginia Tech Elizabeth Scott Tue, 02/24/2015 - 09:15

Monitoring and Improving Surgical Quality

Some statistical issues related to the monitoring of surgical quality will be reviewed in this presentation. The important role of risk-adjustment in healthcare, used to account for variations in the condition of patients, will be described. Some of the methods for monitoring quality over time, including a new one, will be outlined and illustrated with examples. The American College of Surgeons National Surgical Quality Improvement Program (NSQIP) will be described, along with a case study demonstrating significant improvements in surgical infection rates and mortality.

David Sprott Distinguished Lectures

David Sprott distinguished lecture by Raymond J. Carroll, Texas A&M University

Constrained maximum likelihood estimation for model calibration using summary-level information from external big data sources.

David Sprott distinguished lecture by William Woodall, Virginia Tech

Monitoring and Improving Surgical Quality