Statistical methods for administrative data

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As a biostatistician, I develop novel statistical methods to improve the design and analysis of studies and work collaboratively on many projects. While my proposal features topics that are motivated by real data and problems related to disease cluster detection, correlated data, and administrative data sources, my methodology could be applied to many other areas in science, engineering, and industry. Disease cluster detection methods are a special type of spatial statistical method that can be used to identify geographic areas with excess cases of disease (called clusters or hot spots). These methods are important for health authorities to be able to monitor populations for diseases in order to target areas for further investigation or intervention. My recent work in this area includes creating a cluster detection method that detects suspected clusters of disease-related events such as emergency department (ED) visits. My proposed research in this area will incorporate time into the cluster detection methods, adapt methods to include errors in administrative data, and determine criteria when rate calculations would agree with detection. The work will include algebraic derivations and manipulations, and computer simulation studies. These new advances will be important for researchers and administrators conducting surveillance of disease or disease-related events, a timely topic with recent pandemic concerns. Other types of correlated data are also of interest for additional research objectives. When multiple responses can be obtained from individuals, like multiple ED visits, the data are correlated and traditional approaches of testing association between two variables will not be valid. I propose to provide new statistical tests for this situation using a resampling method so that strict distributional assumptions are not required. When multiple administrative data sources are combined, and it is desirable to classify subjects into groups, I will investigate principal component analyses. These objectives will also include algebraic derivations and manipulations and computer simulation studies. My research will provide new and valid approaches for assessing associations and classifying individuals into groups, key goals for many applied research projects.