Data Quality Standards

Abstract Evaluating the quality of clinical data is the basis of achieving accurate conclusions in rejecting or not rejecting the null hypothesis for a research or study (Hattemer-Apostel et. al., 2007). Hence, it is important to achieve the highest accuracy standards in a regulatory or academic study setting. Perhaps, developing and maintaining such principle in clinical studies will make a research credible when evaluating the quality of clinical trial data for a regulator-application or just for academic publications or informational purposes. In addition, other aspects that facilitate the level of evaluation for quality clinical-trial data includes but are not limited to transparency, traceability, identification of critical variables, completeness of information, description of statistical methods, meeting regulatory requirements, great database structure, quality control, maintaining plausible checks, content and scope of source data verification, accurate conclusion, statistics and database management (Hattemer-Apostel et.al., 2007). These are some potential differences or factors between the

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