The Theoretical And Practical Study Of Methods Of Quantifying Clinical Research Data Quality

Nowadays medicine has been transforming from Experienced-based Medicine toEvidence-based Medicine. It is Doctors’ mission to provide their patients with best service.What best service is determined by evidence, which largely built on analyses andevaluation of available clinical study data and outcomes? The data quality of clinicalstudies and the reliability of their conclusions which are used in systematic analysis andmeta-analysis are the basses of scientific integrity of research conclusions ofEvidence-based Medicine.With western countries began to embrace nature and increasingly understanding theconnotation of traditional medicine, Traditional Chinese Medicine (TCM) is usedincreasingly in many countries including developed countries. Widely used, however, TCMhave yet got acknowledgement in the field of western medicine, and was classified as"medical technology or method lack of effective scientific evidence".Scientific and precise clinical study of TCM therefore has been promoted to animportant position. TCM clinical study should not only follow Good Clinical Practice(GCP), scientifically designed in terms of both medicine and statistics, but also implementClinical Data Management (CDM). CDM is involved in all aspects of clinical trial from thebeginning to the end. CDM may guarantee the timeliness, accuracy, authenticity andintegrality of data obtained in TCM clinical study.As an institution of TCM clinical study, we have been tracing the development ofregulations and guidelines on CDM both home and abroad and applying acquiredknowledge into our TCM clinical study practices for years. We also have explored latesttechnologies of CDM, utilized these technologies, developed CDM-related computersystems, and improved our operating procedures to streamline CDM work flow in TCMclinical studies, so as to improve data quality and work efficiency.On the basis of above accumulated experiences, one of the hot topics in the area ofinternational CDM——quantitative evaluation of clinical study data quality arouses ourattention. With the expansion of CDM in TCM clinical study, we have realized that quantifying data quality of clinical study is not only the metrics for evaluating the CDMperformance, but also important for stipulating data quality criteria in the near future.Therefore, we conducted both theoretic and practical studies on quantitative evaluation ofdata quality of clinical study.Theoretical part of this study begins with law and regulations, as well as authoritativeguidelines, discussing and summarizing the requirements of data quality; Overalldiscussion of quantitative evaluation of data quality of clinical study was followed,including reasons and objectives of quantification, definition of quality data and dataerrors, methods of quantitative evaluation of data quality, frequency and occasions for dataquality check, disposal of found errors, acceptable quality level(AQL), and analysis ofpresent situation on this subject both home and abroad.Practical part of the study was conducted in a CRO company in Japan. BackgroundThe metric often used to represent data quality is error rate. During the implementation ofclinical trials, nearly all data transcription and manipulation processes are accompanied byan error rate. Measuring data quality, understanding the errors that are present in clinicaltrial data and preventing errors that have an impact on study results are important activitiesin clinical data management. However, still no established ways existed to measure dataquality, and published error rates are not comparable in the industry. Purpose we tried tomake clear of data quality after double data entry comparing with CRF withreading-through method, and assess the ability of this method to detect errors. Method weexplored data management working records in which reading-through method were usedafter double entry to assure data quality. All errors found and corrected were left with audittrails in these records. We picked up errors found mainly by reading-through method,counted fields in which keyed data are included to calculate error rates after double entrycomparing with CRF. Results We saw a 0.01631％error rate for overall variables,0.01429％for critical variables and 0.01801％for non-critical variables in our study, andreading-through method detected 99.66％of the errors. Limitation The data qualitydiscussed here is only limited to one process of data management (data entry). The moreimportant metric for clinical data quality is Residual Error Rate (RER)Measuring RER andevaluating its impact on clinical trial results need further efforts. ConclusionReading-through method can be applied to measure data quality. Attention should be givento layout design of data sheet, taking into account of legibility for readers, reflection of truedata point on CRF, and cost for printout. Although, we have carried out CDM in TCM clinical study for several years, gap stillexists between our country and developed countries in terms of CDM. Even in abroad,there are no established method to quantify data quality of clinical study and acceptablequality level for data quality, quantitative evaluation of data quality may facilitate thenormalization of CDM process, thus the development of CMD in our country needs topromoted by quantitative evaluation of data quality; at the same time, CDM efforts shouldcreate environment and conditions for quantitative evaluation of data quality of clinicalstudy. So we discussed the feasibilities of implementing quantitative evaluation of dataquality of clinical study in our country, and concluded that the following 4 aspects needmore efforts:①Establishing and improving internal clinical study quality assurance system;②Including provision and planning for quantitative evaluation of data quality in datamanagement plan;③Establishing vertical structural database, standardizing datacollection models;④Constituting independent data quality audit department or post,cultivating professionals in this area,...