THE SOLUTION TO RESOLVE DATA ISSUES

As SAS statistical programmers, you can easily write programs to list all unique values of the gender variable, for example, to inform the team that an invalid value exists for that variable. Once you can isolate clinical data issues, they become ‘known’ and can be ‘accounted for’ to explain differences in expectations and conflicts. Implementing the clinical data acceptance testing procedure involves developing a collection of single purpose macros with basic requirements. Once the system is in place for one clinical study, multiple studies could also be checked as a universal set of macros since the checks are all repetitive and standard.

The benefits of using these macros are increased productivity by quickly and easily apply the macros to other clinical studies, the acceptance of CDM to use the systematic approach method of communicating common issues/concerns, and the biostatistics department having more confidence in the raw clinical data. The end result is that deadlines are not missed since SAS programs do not have to be written defensibly to account for these data issues.

According to the same 2001 survey by the Data Warehousing Institute in figure 2, the benefits of high quality data across all industries can be identified below. During the FDA submission process, a single version of the truth and increased customer satisfaction are very important to recognize reduced costs and minimum delays to get the drug approved. These outcomes are well worth the average cost of $20 to $25 per case report form page or up to 15 % of the clinical research budget to ensure data quality.

Overall, the process flow consists of accessing raw data, which may contain invalid data, with edit check macros to monitor data issues so that only valid data is used in the final analysis data sets, tables, lists and graphs. With this solution, if invalid data is used in the outcome, then the unexpected results can be explained.

Specifically, the solution involves these four steps before having the database lock:

1. Specifying Requirements in Data Management Plan (DMP)

2. Developing and Testing Edit Check Macros

3. Communicating Results with Clinical Data Management (CDM)

4. Monitoring the Metrics of Data Issues