Data quality assurance and curation of large observational clinical studies are difficult, particularly when the data to be collected are heterogeneous. Such studies may consist of a diverse mixture of data types as well as a combination of single, repeated measures as well as time series which may have an irregular sampling. The involvement of multiple sites, particularly where these are international, may introduce further data variances due to local interpretation of procedures and linguistic misunderstandings. As a result, the curation of resultant datasets may be very complex and involved. Poor attention to detail from design through execution including quality control and curation may severely limit data interpretation and consequently re-use and transparency. Furthermore, the lack of attention to accurate meta-data documentation and failure to adhere to data definitions may severely compromise data quality and hamper quality control at study run-tim
The Data Access Quality and Curation for Observational Research Designs (DAQCORD) tool aims: to provide a framework/toolkit for robust study design (and electronic case report form- eCRF- design in particular) and quality management; to provide a framework by which early study plans can be systematically appraised (for example by funding organisations) in terms of their approach to data quality; and to provide a reporting framework with which to describe the steps taken to ensure data quality.
- Planning to start a new clinical study and at the designing phase, would like to know the necessary aspects to ensure the data quality of the study.
- Completed a large, observational clinical study, would like to report it to enhance data sharing and reuse by addressing potential concerns from external investigators about the data quality