The Biostatistics and Respiratory Imaging Core (BRIC or Core) provides the Program Project investigators withsupport in study design, sampling, data management, and statistical data analysis for publication. Complexmodeling 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 studies2) Provide data management skills and resources for Center projects3) Plan and conduct statistical analyses aside from complex modeling for research projects4) Plan and conduct cross-project data analyses for non modeled data generated by the projects and dataresulting 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 fromengaging in complex kinetic or dynamic modeling strategies if such help is requested by any investigator. Coresupport for each project is led by Dr. Al Bartolucci. He has over 25 years of experience as a statisticalcollaborator in cancer and environmental research and is also the developer of Bayesian methodologies forboth clinical and environmental statistical applications. He has worked with correlated data models includingGEE models. Also Dr. Bartolucci has managed several data coordinating centers for cancer clinical trials andVA Gulf War veteran studies. He is associated with personnel having expertise in the areas of highperformance computing, database management and statistical analysis. Dr. Bartolucci has also spentconsiderable time with Dr. Postlethwait in the sample size and power considerations as well as analysis of hisproject on Biochemical Determinants (Project 1 of this proposal). This issue is discussed below for theupcoming grant period.Data management support activities include developing databases for all projects, establishing datadictionaries for easy reference by key project personnel, implementing automatic and manual quality controlchecks that ensures high quality data for analysis. Once a set of data has passed all quality control checks, itwill be 'signed-off on' as ready for analysis. Any changes to the data from that point on will carefullymaintained using an audit trail procedure documenting the change. There are two key advantages incoordinating the data management through a single individual who is a member of the UAB BiostatisticsDepartment: 1) it will be easier to merge datasets across projects, which may be done to explore similarities inresponse to various measures that are used by several projects; and 2) our personnel are experienced inanalytic 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 ofproject 4 in relation to projects 2 and 3 is to apply the integrated model to establish nasal and lower airwaydose-response relationships for site specific histochemical endpoints and evaluate the nose as a possiblesentinel 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 statisticaltechniques for this project including, but not limited to, correlation analysis, prediction modeling (both linear andnon-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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