Study On Data Query Analysis And Data Verification Optimization Of Bioequivalence Clinical Trials

Objective:1.To study the impact of EDC system’s involvement in BE projects on the quality by comparing the difference in data between paper projects and EDC projects.2.To analyze the distribution of data between different modules and of data questioned in the process of manual verification in order to provide data support for future clinical trial data management and clinical trial data verification management.3.To explore the possibility of optimizing data verification.To put forward suggestions and countermeasures for further improvement of data verification in EDC system.Method:The first part: number of data queries between the traditional paper project and the EDC project were compared.Exploratory analysis and Pareto(Pareto)rule was conducted to compare 10 traditional paper project and 10 EDC project,.The second part: Frequency of different query types was compared between the traditional paper project and the EDC project using chi square test.Combined with the results from the first part,effect of early or late involvement of EDC system to a project on the quality of data verification was explored.The third part: number and distribution of each query type in the 20 items were counted respectively.The fourth part: queries sent by artificial inspection were analyzed for all projects.The fifth part: according to the data analysis results of the first four parts,the data inspection optimization was discussed.Result:A total of 21144 queries were received for 20 projects managed by the GOOCLIN system were analyzed,including 10 traditional paper projects and1.By comparing 10 traditional paper projects and 10 EDC projects,it is found that among the traditional paper projects,the inter module query accounts for 19.7%,the single variable entry query accounts for 14.3%,the time type query is 11.2%,the single variable time query accounts for 9.2%,the single variable continuous abnormal value is 7.4%,the single variable time type is 7.4%.Outliers accounted for 6.6%,and these types of queries accounted for the top 80% of total queries.Among the EDC projects,there were 15.6% inter module queries,14% between module time queries,12.2% for single variable entry queries,11.6% among modules,7.2% for single variable time type,5.3% in single variable selection type,4.6% in module time query,and 4.5% for one continuous variable abnormal.Univariate time variant anomalies accounted for 4.6%,which accounted for the top 80% of total EDC projects questioned.2.In this part of the type query significance analysis,when taking(a=0.05),we get the data types that have significant differences in the traditional paper and EDC projects,including the absence of time type variables(P <0.05)the correlation logic query between modules(P<0.05).No significant difference was found in other types of questions.Combined with the actual data comparison,In these two types of queries,the number of queries in EDC projects is less than that in paper projects.3.Because of the particularity of BE test,it focuses on the collection of safety data,life signs(52.3%),blood sample collection(8.8%)has more times of measurement,so the percentage of total data points is higher.4.There were a large number of abnormal data(64.34% and 55.9% in physical examination,respectively)for vital signs and physical examination.5.For most modules,the manual query is very effective for the logical query between the detected modules(the logical query between modules accounts for 74.6%of the total artificial query),and the logical anomaly data between modules cannot be detected effectively,and the manual verification and construction of the original data must be relied on.The logical analysis between modules established after the program is familiar.Conclusion:1.In the data query and comparison of various types of paper projects and EDC projects,the types of data questioning with significant differences are single variable time loss and inter module association queries,and all EDC projects are superior to the traditional paper projects.2.In the data query comparison between 10 traditional paper projects and 10 EDC projects,the types of data questioned with significant differences are single variable time loss and inter module correlation query.And all of the EDC projects are better than the traditional paper projects.Other types of queries have not yet found differences.It proves that the EDC system performs well when dealing with these two kinds of data discrepancy.3.The most questioned modules in the module are the vital sign module and the blood sample collection module.In these two modules,the maximum proportion of the associated queries(the associated queries in the module and the associated query between the modules)is the largest proportion of the whole question,which is due to the lack of overall consideration when the researchers or the CRC input the data for the first time.4.For most modules,manual query is very effective for logical queries between detection modules.Innovation point:1.The data management project of domestic EDC system with independent intellectual property rights is selected to carry out the data management project of the BE test and the data query analysis of the EDC project,and the data management process is discussed with the data query analysis.2.In the data query of 10 traditional paper projects and 10 EDC projects,the data query types with significant differences are single variable time loss and inter module correlation query,and all of the EDC projects are superior to the traditional paper projects.No differences were found for other type of queries.It proves that the EDC system performs well when dealing with these two kinds of data discrepancy.