This document discusses approaches for analyzing spontaneously reported adverse events from post-market drug surveillance. It describes how clinical trials provide an incomplete safety profile and how data from post-market use can reveal rare or long-term safety issues. Statistical methods like disproportionality analysis are used to detect unexpected drug-event combinations in spontaneous reporting data by comparing the frequency of reports for a drug-event pair to what would be expected based on overall reporting rates. Stratifying the data by patient characteristics can improve the accuracy of these analyses. Signals of disproportionate reporting are defined as drug-event pairs where the confidence or credible interval for their association exceeds a threshold value.

1. Copyright © 2010 SAS Institute Inc. All rights reserved.
Visual Analytic Approaches for the Analysis
of Spontaneously-Reported Adverse Events
in Post-Market Surveillance
Richard C. Zink, Ph.D.
Principal Research Statistician Developer
JMP Life Sciences
richard.zink@jmp.com

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Copyright © 2010, SAS Institute Inc. All rights reserved.
Safety Analyses
 Randomized clinical trials are the gold standard for
evaluating the efficacy of a new intervention
 Development program provides an incomplete safety
profile
 Numerous safety endpoints (AE, labs, vitals, hospitalizations,
QOL, ECG; multiplicity)
 Rare safety events require greater sample size (power)
 Studied in limited population
» Those most likely to respond for efficacy
» Those on few or no medications with limited other
confounding diseases
 Animals models may not be predictive (DILI)
 Limited understanding of biological mechanisms and pathways

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Safety Recalls
 Vioxx (rofecoxib). COX-2 inhibitors have increased CV
risk and life-threatening GI bleeding
 Avandia (rosiglitazone). Increased CV risk.
 Rezulin (troglitazone). Liver failure
 Tysabri (natalizumab). Increased risk of PML
(progressive multifocal leukoencephalopathy)
 Risk a therapy may pose may not be well understood
until it has been on the market for many years, taken by
individuals who differ from those studied under the
inclusion criteria of the clinical development program

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Spontaneously-reported adverse events
 Collected by regulatory agencies, pharmaceutical
companies and device manufacturers to monitor the
safety of a product once it reaches the market
 FDA maintains
 AERS: Adverse Event Reporting System
 VAERS: Vaccines Adverse Event Reporting System
 MAUDE: Manufacturer and User Facility Device Experience
 Data are obtained from physicians, patients or medical
literature

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Data Structure
Case
Event 1
Event 2
Event 3
Drug 1
Drug 2
Drug 1
Drug 1
Drug 2
Drug 3
Figure 1. Data Structure
One record (row) per case-event-drug in a SAS formatted data set.
This example would have 6 rows.

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Spontaneously-reported adverse events
 Unique challenges
 No measure of total exposure (total number of subjects taking
the drug)
 No “control”
 Data often inconsistently captured and of poor quality
 Of reported events, is a drug-event combination
occurring more often than expected assuming
independence between drug and events?
 Important:
 No guarantee drug caused event
 Number of cases not equal to number of people. Can get
multiple reports from same person
 Cannot calculate incidence

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Pharmacovigilance: Disproportionality Analysis
Event of interest
Drug of interest Mentioned Not mentioned
Mentioned 𝑛ℎi𝑗 𝑛ℎ𝑖. − 𝑛ℎi𝑗 𝑛ℎ𝑖.
Not mentioned 𝑛ℎ.𝑗 − 𝑛ℎi𝑗 𝑁ℎ − 𝑛ℎ𝑖. − 𝑛ℎ.𝑗 + 𝑛ℎi𝑗 𝑁ℎ − 𝑛ℎ𝑖.
𝑛ℎ.𝑗 𝑁ℎ − 𝑛ℎ.𝑗 𝑁ℎ
Table 1. Contingency Table for Drug i and Event j in Stratum h
Nh is the number of cases in stratum h.
Use the above contingency table to define measures of disproportionality
1. Reporting Odds Ratio (Meyboom et al., 1997)
2. Proportional Reporting Ratio (Evans, Waller & Davis, 2001)
3. Multi-item Gamma Poisson Shrinker (DuMouchel, 1999)
4. Bayesian Confidence Propagation Neural Network (Bate et al., 1998; Gould, 2003).
Stratification is an important consideration for disproportionality analysis. How drugs are
prescribed, disease severity and how individuals may respond to treatment can all be
influenced by demographic and other background characteristics. Therefore,
disproportionality statistics should be calculated among homogeneous groups to avoid
inappropriate conclusions (Woo et al., 2008).

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Pharmacovigilance: Disproportionality Analysis
 How is a signal defined?
 Each drug-event pair has a confidence or credible interval
associated with it
 If this interval exceeds 1, it is an indication that the event occurs
more often for the drug
 May want a higher threshold
 May include frequency of events