The Biostatistics and Respiratory Imaging Core (BRIC or Core) provides the Program Project investigators with? support in study design, sampling, data management, and statistical data analysis for publication. Complex? modeling is best handled within the individual projects as is well documented in each project.
The specific aims ? of this core are to:? 1) Provide expertise in study design and statistical planning of research studies? 2) Provide data management skills and resources for Center projects? 3) Plan and conduct statistical analyses aside from complex modeling for research projects? 4) Plan and conduct cross-project data analyses for non modeled data generated by the projects and data? resulting from modeling within the projects.? The statistical needs of the grant are in three primary areas: multivariate statistics, analysis of correlated data,? and flexible regression modeling including non linear modeling. This does not preclude this Core from? engaging in complex kinetic or dynamic modeling strategies if such help is requested by any investigator. Core? support for each project is led by Dr. Al Bartolucci. He has over 25 years of experience as a statistical? collaborator in cancer and environmental research and is also the developer of Bayesian methodologies for? both clinical and environmental statistical applications. He has worked with correlated data models including? GEE models. Also Dr. Bartolucci has managed several data coordinating centers for cancer clinical trials and? VA Gulf War veteran studies. He is associated with personnel having expertise in the areas of high? performance computing, database management and statistical analysis. Dr. Bartolucci has also spent? considerable time with Dr. Postlethwait in the sample size and power considerations as well as analysis of his? project on Biochemical Determinants (Project 1 of this proposal). This issue is discussed below for the? upcoming grant period.? Data management support activities include developing databases for all projects, establishing data? dictionaries for easy reference by key project personnel, implementing automatic and manual quality control? checks that ensures high quality data for analysis. Once a set of data has passed all quality control checks, it? will be """"""""signed-off on"""""""" as ready for analysis. Any changes to the data from that point on will carefully? maintained using an audit trail procedure documenting the change. There are two key advantages in? coordinating the data management through a single individual who is a member of the UAB Biostatistics? Department: 1) it will be easier to merge datasets across projects, which may be done to explore similarities in? response to various measures that are used by several projects; and 2) our personnel are experienced in? analytic needs of the statistician that will help in database design decisions.? A major thrust will be to support the scientific, statistical and computing needs of project 4. One of the aims of? project 4 in relation to projects 2 and 3 is to apply the integrated model to establish nasal and lower airway? dose-response relationships for site specific histochemical endpoints and evaluate the nose as a possible? sentinel of lower airway effects. Although we will provide the analyses to integrate the goals of these projects,? our statistical capabilities are outlined below referenced for all the projects. We will utilize several statistical? techniques for this project including, but not limited to, correlation analysis, prediction modeling (both linear and? non-linear) and testing for model validity. The general linear modeling (GLM) and non linear modeling (NLIN)? procedures of the SAS software (Statistical Analysis System) version 9.1 are available for most applications.?
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