THE PROBLEM WITH DATA ISSUES

In general, the CDM department may not spend enough resources to check the quality of the data. This is because CDM’s main responsibility is to collect and structure the incoming data. Since the biostatistics department is generally responsible for the final study results, they must often exercise control on data quality before accepting the raw clinical data. The problem often occurs when SAS statistical programmers and statisticians in the biostatistics department process the original ‘unchecked’ clinical data to get incorrect results and conclusions. For example, even

simple checks such as viewing invalid values for the variable gender are not performed. This could result in confusion and frustration.

According to the 2001 survey by the Data Warehousing Institute in figure 1, the sources of data quality problems across all industries can be identified below. It is interesting to note that while most data issues are caused by data entry errors, there is still a substantial amount of data issues that are caused by system related changes, conversions or errors. This indicates that similar types of validation checks should be applied throughout the process of data collection, storage, ransfer, conversion and update. For clinical trials, various studies suggest that up to 5 percent of raw data values in clinical trial databases are erroneous initially.

Examples of using ‘unchecked’ data that resulted in significant delays and costs include:

• In February 2003, the U.S. Treasury Department mailed 50,000 Social Security checks without a beneficiary name. The missing names data issue was due to a software program maintenance error.
• In October 1999, the $ 125 million NASA Mars Climate Orbiter, an interplanetary weather satellite, was lost in space due to a data conversion error. The data issue was due to performing certain calculations in English units (yards) when it should have used metric units (meters).

Specifically, this paper will review an effective method to implement a clinical data acceptance testing procedure to check data quality with each data transfer, conversion or update. The two main categories of clinical data issues may be grouped as incorrect and incomplete data. In general, incorrect data issues consist of unexpected raw values, invalid raw values, incorrect conversion of raw values or inconsistent raw values with another variable or record. Also, incomplete data issues consist of missing values when required.