A Method for the Minimization of Competition Bias in Signal Detection from Spontaneous Reporting Databases

Drug Saf. 2016 Mar;39(3):251-60. doi: 10.1007/s40264-015-0375-8.


Introduction: The two methods for minimizing competition bias in signal of disproportionate reporting (SDR) detection--masking factor (MF) and masking ratio (MR)--have focused on the strength of disproportionality for identifying competitors and have been tested using competitors at the drug level.

Objectives: The aim of this study was to develop a method that relies on identifying competitors by considering the proportion of reports of adverse events (AEs) that mention the drug class at an adequate level of drug grouping to increase sensitivity (Se) for SDR unmasking, and its comparison with MF and MR.

Methods: Reports in the French spontaneous reporting database between 2000 and 2005 were selected. Five AEs were considered: myocardial infarction, pancreatitis, aplastic anemia, convulsions, and gastrointestinal bleeding; related reports were retrieved using standardized Medical Dictionary for Regulatory Activities (MedDRA(®)) queries. Potential competitors of AEs were identified using the developed method, i.e. Competition Index (ComIn), as well as MF and MR. All three methods were tested according to Anatomical Therapeutic Chemical (ATC) classification levels 2-5. For each AE, SDR detection was performed, first in the complete database, and second after removing reports mentioning competitors; SDRs only detected after the removal were unmasked. All unmasked SDRs were validated using the Summary of Product Characteristics, and constituted the reference dataset used for computing the performance for SDR unmasking (area under the curve [AUC], Se).

Results: Performance of the ComIn was highest when considering competitors at ATC level 3 (AUC: 62 %; Se: 52 %); similar results were obtained with MF and MR.

Conclusion: The ComIn could greatly minimize the competition bias in SDR detection. Further study using a larger dataset is needed.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adverse Drug Reaction Reporting Systems / standards
  • Adverse Drug Reaction Reporting Systems / statistics & numerical data*
  • Bias*
  • Databases, Factual / standards
  • Databases, Factual / statistics & numerical data*
  • Drug-Related Side Effects and Adverse Reactions / diagnosis
  • Drug-Related Side Effects and Adverse Reactions / epidemiology*
  • Humans
  • Pilot Projects