The main purpose of the Biostatistics and Data Management Core (Core C) 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. A secondary role will be to resolve any methodological issues resulting from the analyses of the Program Project data. The overarching goal of our PPG is to identify pathways that account for the synergistic effects of stress, obesity and diet in the development of hypertension that are open to therapeutic intervention. Different types of investigations are being proposed in this project to accomplish this goal. This will require distinctly different design and analytical methods. The Biostatistics and Data Management Core 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 Core will also serve as the focus for data compilation, quality control, data management, data analysis and interpretation support for the PPG. To that extent, we propose the following three Specific Aims: (1) Design and apply analytical methods to be used in the quantitative studies associated with each of the projects in the PPG; (2) 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, interpretation of results, and preparation of manuscripts for presentations and publications; and, (3) Maintain a dataflow system that insures accuracy, security, validation and archiving of all data collected for the projects of the PPG.
The main goal of this PPG is to identify the mechanisms underlying the most important risk factors of hypertension. It is critically important to have the correct study designs, statistically valid and efficient analytical techniques and meaningful correlation of statistical and clinical conclusions in order to establish these mechanisms. Statistically valid identifications of the mechanisms of the hypertension risk factors will h have significant public health implications.
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