QPPV.COM KNOWLEDGE ASSESSMENT

Disproportionality Analysis in Pharmacovigilance: Knowledge Assessment

Test your understanding of the concepts covered by Disproportionality Analysis in Pharmacovigilance.

Question 1 of 10Pass mark: 80%

Question 1 of 10 What is the primary purpose of disproportionality analysis in pharmacovigilance?

To prove that a medicinal product caused a reported adverse event To identify product-event combinations reported more often than expected for further evaluation To estimate the incidence of an adverse event in the treated population To replace clinical review of individual case safety reports

Question 2 of 10 Which sequence best represents the role of disproportionality analysis within signal management?

Signal assessment, disproportionality analysis, signal validation, regulatory conclusion Database extraction, regulatory action, disproportionality analysis, case review Disproportionality analysis, statistical flag, signal validation, signal assessment Clinical trial analysis, disproportionality analysis, causality confirmation, signal closure

Question 3 of 10 Why are both cumulative and interval analyses useful in routine signal detection?

Cumulative analyses identify trends while interval analyses always establish causality Interval analyses can identify emerging changes, while cumulative analyses provide the broader historical pattern Cumulative analyses are used only for clinical trials and interval analyses only for spontaneous reports They produce identical results but satisfy different formatting requirements

Question 4 of 10 Which statement best reflects the regulatory significance of an EVDAS or similar disproportionality output?

It is a definitive regulatory finding requiring immediate product withdrawal It is a screening output that requires follow-up and clinical interpretation It replaces the need for MAH signal management procedures It is equivalent to an observed incidence rate in the European population

Question 5 of 10 Which action is most directly intended to investigate masking in a spontaneous reporting database?

Sequentially removing strongly reported product-event combinations and reassessing other pairs Restricting analysis to reports from consumers only Replacing all observed counts with expected counts Discarding events with a positive IC

Question 6 of 10 Which set of records most strongly supports reproducibility during an inspection?

Only the final ranked signal list The current SOP without the data or software details The dataset snapshot, software and library versions, parameter settings, code history and run output A verbal explanation from the analyst who performed the run

Question 7 of 10 Which threshold is commonly associated with an IC-based signal criterion described in the article?

IC025 greater than 0 IC025 less than 0 ROR lower confidence limit less than 1 PRR exactly equal to 1

Question 8 of 10 Why should the magnitude of an EBGM not be expected to equal the raw observed-to-expected ratio?

EBGM is calculated from exposure data rather than reports EBGM uses empirical-Bayesian shrinkage and therefore adjusts the estimate toward a prior expectation EBGM is expressed as a confidence interval rather than a point estimate EBGM excludes all reports involving serious events

Question 9 of 10 What is the principal conceptual advantage of IC and EBGM compared with unshrunk ratios when counts are sparse?

They eliminate all confounding caused by indication They use shrinkage to reduce instability and noise from small counts They directly measure the clinical severity of the event They calculate population incidence without additional data

Question 10 of 10 A reviewer observes a strong statistical flag for a product and event, but most cases are duplicates. What is the most appropriate first operational response?

Escalate immediately because duplicates strengthen the signal Remove the product from all future analyses Apply a documented deduplication process and reassess the result Treat the result as a confirmed adverse reaction

Disproportionality Analysis in Pharmacovigilance: Knowledge Assessment

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