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Signal Detection for Recently Approved Products: Adapting and Evaluating Self-Controlled Case Series Method Using a
Xiaofeng Zhou1, Ian J Douglas2, Rongjun Shen3
1Epidemiology, Worldwide Safety and Regulatory, Pfizer Inc, 219 E. 42nd Street, Mail Stop 219/9/01, New York, NY, 10017, USA. xiaofeng.zhou@pfizer.com.
Introduction:
The Self-Controlled Case Series (SCCS) method has been widely used for hypothesis testing, but there is limited evidence of its performance for safety signal detection.
Objective:
The objective of this study was to evaluate SCCS for signal detection on recently approved products.
Methods:
A retrospective study covered the period after three recently marketed drugs were launched through to 31 December 2010 using The Health Improvement Network, a UK primary care database, and Optum, a US claims database. The SCCS method was applied to examine five heterogenous outcomes with desvenlafaxine and escitalopram and six outcomes with adalimumab for Signals of Disproportional Recording (SDRs); a positive finding was determined to be when the lower bound of 95% Confidence Interval of the incidence rate ratio (IRR) estimate was > 1. Multiple design choices were tested and the trend in IRR estimates over calendar time for one drug event pair was examined.
Results:
All six outcomes with adalimumab, three of five outcomes with desvenlafaxine, and four of five outcomes with escitalopram had SDRs. SCCS highlighted all acute events in the primary analysis but was less successful with slower-onset outcomes. Performance varied by risk period definition. Changes in IRR estimates over quarterly intervals for adalimumab with herpes zoster showed marked higher SDR within 9 months of drug launch.
Conclusion:
SCCS shows promise for signal detection: it may highlight known associations for recent marketed products and has potential for early signal identification. SCCS performance varied by design choice and the nature of both exposure and event pair. Future work is needed to determine how effective the approach is in prospective testing and determining the performance characteristics of the approach.
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