Administrative Core

Administrative and biostatistical activities of the Program Project will take place in Core A. Drs. Isales and Hamrick will be responsible for the scientific oversight of the PPG, the direction of research emphasis and the fiscal administration. Both Drs. Isales and Hamrick will work with members and Project and Core Leaders in order to effectively resolve all personnel and fiscal issues that may arise. Both will play an active role in coordinating the mentoring activities for junior faculty and minority summer students. Dr. Isales will oversee compliance with regard to GRU?s Institutional Review Board's policies, including the requirement that all Project and Core investigators and staff maintain certification in GRU?s Research Ethics Training Program. The main purpose of the Biostatistics component is to bring together the information from different sources into an integrated database, and to coordinate the statistical analyses with the focus being on the main goals of the PPG. The Biostatistics faculty will help the investigators of the projects to choose appropriate experimental designs and analytical techniques tailored to address specific hypotheses, especially, focusing on justification of the data analysis method to be used for each type of data generated. The Biostatistics component will serve as the focus for data compilation, quality control, data analysis and interpretation support for the PPG. Thus the overall aims of this Core are: (1) Coordinate and oversee the research efforts of the individual projects to maximize project synergy and ensure administrative and regulatory compliance; (2) Provide training opportunities in aging to junior faculty who are part of the PPG efforts through didactic teaching and individual mentoring; (3) Disseminate the findings obtained by the component Projects and Cores as detailed in the Resource Sharing plan; (4) Coordinate summer rotations for minority students who are interested in aging research; (5) Help investigators of the projects to choose appropriate experimental designs and analytical techniques tailored to address specific hypotheses, particularly focusing on justification of the analysis method to be used for each type of data generated; (6) Provide ongoing statistical consultation to research projects, focusing on issues such as experimental design, sample size, aptness and validity of models to be used, power considerations, deep-sequencing data processing and modeling, interpretation of results, and preparation of presentations and manuscripts for publication; (7) Maintain a dataflow system that ensures accuracy, security, validation and archiving of all data collected for different projects.