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Signal Detection for Recently Approved Products: Adapting and Evaluating Self-Controlled Case Series Method Using a US Claims and UK Electronic Medical Records Database

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Abstract

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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Acknowledgements

We would like to acknowledge OMOP for the open source code that provided the basis for the SCCS implementation code used for this project.

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Authors and Affiliations

  1. Epidemiology, Worldwide Safety and Regulatory, Pfizer Inc, 219 E. 42nd Street, Mail Stop 219/9/01, New York, NY, 10017, USA

    Xiaofeng Zhou, Rongjun. Shen & Andrew. Bate

  2. London School of Hygiene & Tropical Medicine, London, UK

    Ian. J. Douglas

  3. Division of Clinical Pharmacology, NYU School of Medicine, New York, NY, USA

    Andrew. Bate

Authors
  1. Xiaofeng Zhou
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  2. Ian. J. Douglas
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  3. Rongjun. Shen
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  4. Andrew. Bate
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Corresponding author

Correspondence to Xiaofeng Zhou.

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Funding

This study was funded by Pfizer Inc.

Conflicts of interest

Xiaofeng Zhou, Rongjun Shen, and Andrew Bate are full-time employees of Pfizer Inc. and hold stock and stock options. Pfizer manufactures one of the drugs studied herein, desvenlafaxine, as well as other products used to treat primary indications of the other two drugs analyzed herein, namely depression and rheumatoid arthritis. Ian Douglas is a full-time employee of the London School of Hygiene and Tropical Medicine and received no funding from Pfizer for his contribution to this study and article. He is funded by an unrestricted grant from, holds stock in, and has consulted for, GlaxoSmithKline.

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Zhou, X., Douglas, I.J., Shen, R. et al. Signal Detection for Recently Approved Products: Adapting and Evaluating Self-Controlled Case Series Method Using a US Claims and UK Electronic Medical Records Database. Drug Saf 41, 523–536 (2018). https://doi.org/10.1007/s40264-017-0626-y

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