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Open Access Research article

The performance of sequence symmetry analysis as a tool for post-market surveillance of newly marketed medicines: a simulation study

Nicole L Pratt1*, Jenni Ilomäki1, Chris Raymond2* and Elizabeth E Roughead1

Author Affiliations

1 Quality Use of Medicines and Pharmacy Research Centre, Sansom Institute, School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, SA, Australia

2 Department of Health and Ageing, Pharmaceutical Evaluation - DUSC/ESC Section, Pharmaceutical Evaluation Branch, Pharmaceutical Benefits Division, Canberra, ACT, Australia

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BMC Medical Research Methodology 2014, 14:66  doi:10.1186/1471-2288-14-66

Published: 15 May 2014

Abstract

Background

Sequence symmetry analysis (SSA) is a potential tool for rapid detection of adverse drug events (ADRs) associated with newly marketed medicines utilizing computerized claims data. SSA is robust to patient specific confounders but it is sensitive to the underlying utilization trends in the medicines of interest. Methods to adjust for utilisation trends have been developed, however, there has been no systematic investigation to assess the performance of SSA when variable prescribing trends occur. The objective of this study was to evaluate the validity of SSA as a signal detection tool for newly marketed medicines.

Methods

Randomly simulated prescription supplies for a population of 1 million were generated for two medicines, DrugA (medicine of interest) and DrugB (medicine indicative of an adverse event). Scenarios were created by varying medicine utilization trends for a newly marketed medicine (DrugA). In addition, the magnitude of association between DrugA and DrugB was varied. For each scenario 1000 simulations were generated. Average Adjusted Sequence Ratios (ASR), bootstrapped 95% confidence intervals (CIs), percentage of CI's which covered the expected ASR and percent relative bias were calculated.

Results

When no association was simulated between DrugA and DrugB, over 95% of SSA CI's covered the expected ASR (ASR = 1) and relative bias was 1% or less irrespective of medicine utilization trends. In scenarios where DrugA and DrugB were associated (ASR = 2), unadjusted SR's were underestimated by between 11.7 and 15.3%. After adjustment for trend, ASR estimates were close to expected with relative bias less than 1%. Power was over 80% in all scenarios except for one scenario in which medicine uptake was gradual and the effect of interest was weak (ASR = 1.2).

Conclusions

Adjustment for underlying medicine utilization patterns effectively overcomes potential under-ascertainment bias in SSA analyses. SSA may be effectively applied as a safety signal detection tool for newly marketed medicines where sufficiently large health claim data are available.