CTRSU Research Areas

The CTRSU is involved in the research areas of Computational Epidemiology, Data Visualization, Visual Analytics, Data Integration and Application Development, and Geospatial Epidemiology.

Computational Epidemiology Unit

Members of the CTRSU are experts in a number of specialized areas of biostatistics, bioinformatics, and epidemiology including the following:

  • Sample size estimation
  • Multivariable modeling
  • Machine learning
  • Propensity score analysis
  • Instrumental variables analysis
  • Survival analysis
  • Geospatial analysis

Our team primarily uses the R statistical environment (http://cran.r-project.org) but we each have expertise with a number of different software packages such as SAS, SPSS, Minitab, MedCalc, Python, and ArcGIS.

We offer training on biostatistical methods annually as well as on a case-by-case basis. This includes basic biostatistical methods such as sample size estimation, assessing correlation and association, evaluation of confounding, medical diagnostic accuracy statistics, as well as using statistical programming languages.

Tim L Wiemken, PhD

Data Engineering and Visual Analytics Core

While the primary data collection tool for a study is often a set of paper case report forms, for proper statistical analysis, the data must be converted to an appropriate electronic format that lends itself to searching and sorting the dataset as well as selecting data subsets.

Our team has extensive experience building data collection systems using either the REDCap System developed by Vanderbilty University or from ground up using a tiered client-server or cloud-based design.

Robert R Kelley, PhD

Data Integration and Application Development

Data Integration involves the creation of connections across data type and purpose. It involves making connections between disparate types of data including clinical, biological, and social. Taken together this data begins to form a more complete picture of the mechanisms of disease.

While systems exist to store many different types of data, creating the software to connect these systems is a new and ongoing process. It requires the ability to work with a variety of data, and a variety of software implementations for managing data. This makes application development a key component of any successful data integration pursuit.

William A Mattingly, PhD

Geospatial Epidemiology

Geospatial Epidemiology seeks to define geographic correlations of patients with their socioeconomic (e.g. income) and demographics (e.g. race) attributes.

It is concerned with the description and examination of disease and its geographic variations. This is done in consideration of “demographic, environmental, behavioral, socioeconomic, genetic, and infections risk factors.

Dedicated to improving the quality and efficiency of clinical and translational research at the University of Louisville.

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