Visualizing Missingness and Data Quality

Data quality visualization is particularly useful because it can turn a long query listing into a pattern. For example, a table of 600 open queries may be difficult to interpret, while a bar chart of open queries by site and category may immediately show where attention is needed. The following code summarizes missing values in selected required fields: This plot shows which variables have the most missing values. It does not determine whether the missing values are expected. For example, day 28 outcome may be missing because follow-up is not yet due. A better plot may restrict to participants whose outcomes are due.

This chart is more meaningful because the denominator matches the operational question. Data quality graphics should always be designed around the correct denominator. Query status can also be visualized: This stacked bar chart can show whether sites have many open, answered, or closed queries. If the chart is used for management, it should be accompanied by definitions of query status and reporting date.