This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The success of any multi-center clinical trial depends upon the coherent collection, analysis, and quality of the data. Quality control and assurance is a key to this success. Quality control and assurance is the standardization, validation, and compliance checking of Instrumentation performance, standard operating procedures, data logging, analysis, and reporting. Good quality control and assurance makes good and proper scientific sense. Poor quality control and assurance could result in losses of usable data, incorrect conclusions, taxpayer dollar waste, and may negatively impact future funding decisions related to a research methodology. With a further view to the future, quality control and assurance is a requirement for any possible FDA application, and is a necessary component for trial results to gain acceptance by the medical community. We propose a quality control and assurance project to ensure high data integrity that will support all clinical trial aims. The quality control and assurance project will standardize, coordinate and validate all data acquired and reported in this clinical trial. Standardize and validate calibration standards and procedures. DOS/I requires both frequency domain and spectral calibration procedures using tissue simulating phantoms and reflectance standards, respectively. We will develop robust and standardized calibration phantom fabrication, testing, and measurement procedures. Redundancy will be built into the process, and the integrity of the calibration standards themselves will be validated by each clinical site. We expect that the establishment of calibration standards and procedures will minimize data loss and improve overall data quality.
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