Sequence symmetry analysis in pharmacovigilance and pharmacoepidemiologic studies

Edward Chia Cheng Lai, Nicole Pratt, Cheng Yang Hsieh, Swu Jane Lin, Anton Pottegård, Elizabeth E. Roughead, Yea Huei Kao Yang, Jesper Hallas

研究成果: Review article同行評審

26 引文 斯高帕斯(Scopus)


Sequence symmetry analysis (SSA) is a method for detecting adverse drug events by utilizing computerized claims data. The method has been increasingly used to investigate safety concerns of medications and as a pharmacovigilance tool to identify unsuspected side effects. Validation studies have indicated that SSA has moderate sensitivity and high specificity and has robust performance. In this review we present the conceptual framework of SSA and discuss advantages and potential pitfalls of the method in practice. SSA is based on analyzing the sequences of medications; if one medication (drug B) is more often initiated after another medication (drug A) than before, it may be an indication of an adverse effect of drug A. The main advantage of the method is that it requires a minimal dataset and is computationally efficient. By design, SSA controls time-constant confounders. However, the validity of SSA may be affected by time-varying confounders, as well as by time trends in the occurrence of exposure or outcome events. Trend effects may be adjusted by modeling the expected sequence ratio in the absence of a true association. There is a potential for false positive or negative results and careful consideration should be given to potential sources of bias when interpreting the results of SSA studies.

頁(從 - 到)567-582
期刊European Journal of Epidemiology
出版狀態Published - 2017 七月 1

All Science Journal Classification (ASJC) codes

  • Epidemiology

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