A mathematical framework to quantify the masking effect associated with the confidence intervals of measures of disproportionality

François Maignen, Manfred Hauben, Jean Michel Dogné

Research output: Contribution to journalArticle

Abstract

Background: The lower bound of the 95% confidence interval of measures of disproportionality (Lower95CI) is widely used in signal detection. Masking is a statistical issue by which true signals of disproportionate reporting are hidden by the presence of other medicines. The primary objective of our study is to develop and validate a mathematical framework for assessing the masking effect of Lower95CI. Methods: We have developed our new algorithm based on the masking ratio (MR) developed for the measures of disproportionality. A MR for the Lower95CI (MRCI) is proposed. A simulation study to validate this algorithm was also conducted. Results: We have established the existence of a very close mathematical relation between MR and MRCI. For a given drug–event pair, the same product will be responsible for the highest masking effect with the measure of disproportionality and its Lower95CI. The extent of masking is likely to be very similar across the two methods. An important proportion of identical drug–event associations affected by the presence of an important masking effect is revealed by the unmasking exercise, whether the proportional reporting ratio (PRR) or its confidence interval are used. Conclusion: The detection of the masking effect of Lower95CI can be automated. The real benefits of this unmasking in terms of new true-positive signals (rate of true-positive/false-positive) or time gained by the revealing of signals using this method have not been fully assessed. These benefits should be demonstrated in the context of prospective studies.

LanguageEnglish
Pages231-244
Number of pages14
JournalTherapeutic Advances in Drug Safety
Volume8
Issue number7
DOIs
StatePublished - 1 Jul 2017

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Confidence Intervals
Pharmaceutical Preparations
Prospective Studies

Keywords

  • adverse drug reactions reporting systems
  • masking
  • pharmacovigilance
  • postmarketing
  • product surveillance
  • signal
  • signal detection

Cite this

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abstract = "Background: The lower bound of the 95\{%} confidence interval of measures of disproportionality (Lower95CI) is widely used in signal detection. Masking is a statistical issue by which true signals of disproportionate reporting are hidden by the presence of other medicines. The primary objective of our study is to develop and validate a mathematical framework for assessing the masking effect of Lower95CI. Methods: We have developed our new algorithm based on the masking ratio (MR) developed for the measures of disproportionality. A MR for the Lower95CI (MRCI) is proposed. A simulation study to validate this algorithm was also conducted. Results: We have established the existence of a very close mathematical relation between MR and MRCI. For a given drug–event pair, the same product will be responsible for the highest masking effect with the measure of disproportionality and its Lower95CI. The extent of masking is likely to be very similar across the two methods. An important proportion of identical drug–event associations affected by the presence of an important masking effect is revealed by the unmasking exercise, whether the proportional reporting ratio (PRR) or its confidence interval are used. Conclusion: The detection of the masking effect of Lower95CI can be automated. The real benefits of this unmasking in terms of new true-positive signals (rate of true-positive/false-positive) or time gained by the revealing of signals using this method have not been fully assessed. These benefits should be demonstrated in the context of prospective studies.",
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A mathematical framework to quantify the masking effect associated with the confidence intervals of measures of disproportionality. / Maignen, François; Hauben, Manfred; Dogné, Jean Michel.

In: Therapeutic Advances in Drug Safety, Vol. 8, No. 7, 01.07.2017, p. 231-244.

Research output: Contribution to journalArticle

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