DPAC

The overall goal of the Data Processing and Analysis Core (DPAC) is to provide consistent and cost-effective expertise in biostatistics and research informatics for all projects in the proposed research program of the University of Southern California Tobacco Centers of Regulatory Science (USC TCORS). Key functions and support provided by DPAC include initial consultation on study designs, followed by provision of tools for survey platform and database development, especially tracking and monitoring databases within projects and sharing data across projects; generation of interim reporting on progress for each study; generation of cleaned, coded (consistently across projects), and documented analytic datasets for each project, as well as initial univariate and summary analyses for common data elements. The DPAC team consists of a multidisciplinary group of data specialists and experts who are experienced in the collection, management, and analysis of longitudinal and experimental study data, and reporting these in the published articles. The DPAC will support and facilitate interdisciplinary research by providing essential services related to data management, biostatistics, and integrated analytics to enable modern data-driven investigations. Supporting informatics and biostatistical needs for investigators will include: (1) working with the Administrative Core and Projects to create user-friendly platforms for data collection and sharing; (2) demonstrating and optimizing the process of data accuracy and usability through data cleaning and codebook development; (3) investigating project-specific hypotheses in collaboration with project personnel using analysts and expertise from the Core; and (4) supporting study results interpretation and publication of research studies. To achieve these goals, the DPAC has the following Specific Aims: (1) To work with USC TCORS investigators to develop front-end survey platforms for capturing data and back-end data processing mechanisms to optimize the timeliness, efficiency, accuracy, and usability of data; and (2) To provide biostatistical methodological support for developing and implementing data analysis plans and interpreting data.