Masking is a reporting bias where drug safety signals are muffled by elevated reporting of other medications in spontaneous reporting databases. While impacts of masking are often limited, its effect on restricted designs, such as active comparators, can be consequential. We used data from the United States Food and Drugs Administration Adverse Event Reporting System (1999Q3-2013Q3) to study masking in a real-world example. Rosiglitazone, a thiazolidinedione with elevated reporting after safety concerns over cardiovascular risks, was the masking candidate. We hypothesized stimulated reporting masked signals for another thiazolidinedione, pioglitazone. We computed estimates of proportional reporting ratios and information components, using the Bayesian confidence propagation neural network, for pioglitazone-myocardial infarction and pioglitazone-cardiac failure under unrestricted and active comparator designs, both with and without the mask, and before (1999Q3-2007Q1) and after (2007Q1-2013Q3) safety concerns. Relative change-in-estimates were computed to compare results with and without rosiglitazone. From 1999Q3-2007Q1, relative change-in-estimates of proportional reporting ratio for pioglitazone-myocardial infarction was 0.00 in unrestricted design and 0.10 in active comparator; For pioglitazone-cardiac failure, the change was 0.01 and 0.62, respectively. From 2007Q2-2013Q3, relative change in estimate for pioglitazone-myocardial infarction was 0.41 in unrestricted design and 18.00 in active comparator; the change for pioglitazone-cardiac failure was 0.04 and 1.03, respectively. Relative changes in estimates of information component mirrored these trends. In conclusion, masking can influence signal detection in active comparator designs where external events impact reporting rates in reference sets. Evaluating masking in related contexts is essential for drug safety monitoring and resource allocation for follow-up studies.