Design A CDASH-Based Edit Check Library

Objective:The purpose of this study is to build an edit check library based on Clinical Data Acquisition Standards Harmonization (CDASH) which is under Clinical Data Interchange Standards Consortium (CDISC) so that edit checks needed in clinical trial process are standardized and reusable. That can make data managers spend more time on data management rather than preparation for data management, and finally further accelerate new product to come into the market and shorten time for patients to get appropriate medication. The study is also to get the most reasonable edit check library by comparing with real studies.Method:Research method can be divided into two parts:1) Build edit check libraries. Firstly, research CDASH in details to know what variables should be collected, why they should be collected, and whether there is any logical relationshipbetween thesevariables. Secondly, fully preparefor my owndesign work by studying edit checks in real clinical trials to know what edit checks should be built and why they should be built. Lastly, create edit check libraries, respectively forHRvariables, HR andR/Cvariables, andall variables in CDASH.2) Statistical analysis. Match edit checks in libraries with edit checks in 15 real studies to calculate Coverage Rate and Utilization Rate in every domain. And then draw the most reasonable library by results of one-way ANOVA and K-W test.Result:There are 148,225, and 329 edit checks in Library 1,2, and 3. Interms ofCoverage Rate andUtilization Rate, there are three cases:1) Results of Coverage Rate are totally consistent with results of Utilization Rate. Both Coverage Rate and Utilization Rate of library 3 are superior to the other two libraries in certain domain. Adverse Event (AE) belongs to this case.2) There are significantdifferences on Coverage Rate between libraries, but there is no significantdifference on Utilization Rate. Previousand Concomitant Medication (CM), Disposition (DS):and ECGTest Results (EG) belong tothis case.3) Results of Coverage Rate are completelycontrary to that of Utilization Rate. That is to say library 3 works best on Coverage Rate, but worst on Utilization Rate in certain domain. Demographics (DM), Exposure (EX), Laboratory Test Results (LB), Medical History (MH), Physical Examination (PE), Vital Signs (VS) belong to this case.Conclusion:Suggest to select library 3 under domain (e.g. AE, CM, DS, EG) with consistent results and use library 2 under domains with inconsistent results.