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 regulatory-application study and academic study that could influence differences in both study types.

Data Quality Standards

It is important to have a clear and non-ambiguous research question(s) before starting a clinical research whether the study is a regulatory or academic setting. An academic research environment may desire a broader spectrum of the research question which may lead to follow-up studies. On the other hand, a regulatory-application study specification should not go beyond the intended claims of the study. Hence, a regulatory-application study must be designed properly in capturing sample size, representative of the population and demographics (Hattemer-Apostel et.al. 2007).

Transparency and traceability should be key components or standards employed among the data sources while conducting a regulatory or academic clinical study. The data management team should ensure the system’s credibility in capturing a transparent and traceable data in all accounts (Hattemer-Apostel et.al. 2007). Furthermore, identification of critical variables or data (adverse events, target cells or organs, etc.) is a crucial component in the regulatory and an academic study-settings. Hence, the accuracy in the identification of critical variables enhance the precision in the identification and verification process of adverse events, and consequently result in an in-depth information of the efficacy and safety of the investigational medicinal product (Hattemer-Apostel et.al. 2007). Perhaps, investigational medicinal products are approved or disapproved based on efficacy and safety tendencies.

Maintaining complete information is required in both a regulatory-application study and academic study. The regulatory bodies do not accept partial information on the efficacy or safety of any study or investigational products for approval purposes. In furtherance, all relevant data captured in a regulatory-application study must be included in the report, but not all non-critical data should be analyzed. Hence, only relevant and critical data should be analyzed. The ICH-E3 recommendation on the important components of report-documents are listed in annex1, section 16.2 of the ICH guidelines (ICH. 1996).

An academic study intended for publication is not required to include all data in the publication. For a regulatory study application, a strict description of statistical method is outlined in the statistical analysis plan (SAP), and prior to statistical analysis plan, the clinical data must be on a lock stage. In addition, regulatory requirements such as the ICH-GCP data management guidelines must be maintained (Hattemer-Apostel, R., et.al. 2007). The statistics, data management, and database structure must ensure a clean database for the purpose of statistical analysis in order to provide credible results and conclusions. In part, this could be achieved by clearly defining and maintaining plausible checks in the data validation plan (DVP). Therefore, the primary and secondary efficacy and safety data must be clearly defined to show and identify any statistical and/or clinical significance of the investigational medicinal product (Hattemer-Apostel et.al., 2007). In contracts, a study intended for publication purposes does not have to show any clinical significance.

Thus, the quality control and content/scope of source data verification (SDV) must be based on the acceptable guideline-standards including credible coding and standardization of data (Carlin, 2012). In addition, 100% data check including case report forms (CRFs) in all the investigator’s site is desirable for a regulatory-application study (Barton, Carlin, & Wilkinson, 2012). Nonetheless, in a situation where 100% data quality assurance error is not attainable or feasible for all the CRF fields or other documents, data with less than 10% data error may be acceptable depending on the study or the intended use of the study (Hattemer-Apostelet.al., 2007).

References

Barton, B., Carlin, D., & Wilkinson, S. (2012). Data cleaning and queries. Retrieve from https://class.waldenu.edu/webapps/portal/frameset.jsp?tab_tab_group_id=_2_1&url= %2Fwebapps%2Fblackboard%2Fexecute%2Flauncher%3Ftype%3DCourse%26id %3D_1958012_1%26url%3D.

Carlin, D. (2012). Data Quality and errors. Retrieve from https://class.waldenu.edu/webapps /portal/frameset.jsp?tab_tab_group_id=_2_1&url= %2Fwebapps%2Fblackboard %2Fexecute%2Flauncher%3Ftype%3DCourse%26id%3D_1958012_1%26url%3D.

Hattemer-Apostel, R., et. al. (2007). Getting better clinical trial data. An inverted viewpoint. Retrieve from http://www.diahome.org/Tools/Content.aspx?type=eopdf&file= %2Fproductfiles%2F8357%2Fdiaj_22313.pdf.

ICH. (1996). Guideline for industry. Retrieve from http://www.fda.gov/downloads /Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM073113.pdf.