CDISC

There have been many innovations in the field of data collection and analysis in clinical trials. These innovations include clinical monitoring, case report forms, electronic data capture, and clinical study databases. However despite these innovations there was still a need to standardize data collection practices. The need is to make data interchangeable and accessible to researchers with new hypotheses.

CDISC was formed in 1997 “to develop and support global, platform-independent data standards that enable information system interoperability to improve medical research and related areas of healthcare.”

The members of the CDISC consortium are part of the following professions:

. Pharmaceutical companies

. Medical device manufacturers

. Regulatory authorities

. Service providers

This consortium publishes standards that are recommended for clinical trial data to further the goal of interoperability.

The clinical data acquisition standards harmonization (CDASH) was formed to standardize the data collection. When data is collected from a clinical trial, it is entered into a database. Each item in the database normally includes unique identifier (For example: patient ID in the event that we have one row per subject). Each piece of information is placed in a place called a “field” . Values from the fields can be extracted for analysis. When we meet the CDASH standard we must specify the name and type of fields. The CDASH standard also specify how the fields are organised. The CDASH standard is used for developing case report form and electronic data capture system.

Next step after CDASH is to convert into standard datasets. This is the SDTM standard. This is done in order to be used for analysis. The concept is that each data point is to be uniquely identified based on corresponding information (For example: patient ID in the event that we have one row per subject). Thus each row contains one piece of data and as many columns as there is identifying information. The data in SDTM are separated into multiple “domains” such as demographics (DM), exposure (EX), adverse events (AE)… The file structure is one data file per domain. These SDTM data sets can be used directly for analysis if no further calculation or derivation is necessary.

The analysis data model (ADaM) have a clear lineage from data collection to analysis. The ADaM datasets are utilised for all data derivations used in statistical analyses. For example, if we want to derive change from baseline for body weight we would have to start with the SDTM and it would contain each body weight measurements at each visit when this parameter was collected.

The ADaM data sets are not required unless there is derivation performed from the SDTM dataset. Derivation in ADaM cannot have any other source except from SDTM dataset.