Sequence symmetry analysis in pharmacovigilance and pharmacoepidemiologic studies

Abstract

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.

A sequence symmetry analysis of the interrelationships between statins, diabetes and skin infections

“…The SSA was originally used as an economical and rapid means of reviewing adverse drug reactions using prescription drugs . The analysis was later expounded, and has since gained popularity in pharmacoepidemiology to detect adverse events .…”

“…The SSA was originally used as an economical and rapid means of reviewing adverse drug reactions using prescription drugs . The analysis was later expounded, and has since gained popularity in pharmacoepidemiology to detect adverse events . Advantages of the SSA over other epidemiological study designs include controlling for confounding factors that do not vary considerably over the study period, such as age, sex or genetics …”

“…7 The analysis was later expounded, 8 and has since gained popularity in pharmacoepidemiology to detect adverse events. 7 Advantages of the SSA over other epidemiological study designs include controlling for confounding factors that do not vary considerably over the study period, such as age, sex or genetics. 7,9 To detect adverse events using SSA, the sequence of incident (firsttime) prescriptions of patients taking both the drug of interest (index drug) and the drug specifically indicated for treating the adverse event (marker drug) is examined.…”

“…7 Advantages of the SSA over other epidemiological study designs include controlling for confounding factors that do not vary considerably over the study period, such as age, sex or genetics. 7,9 To detect adverse events using SSA, the sequence of incident (firsttime) prescriptions of patients taking both the drug of interest (index drug) and the drug specifically indicated for treating the adverse event (marker drug) is examined. 8,9 Prescription sequences with intervals greater than 365 days between the index and marker drugs were not analysed to minimise potential time-varying confounders such as age.…”

“…We thank the Australian Department of Veterans' Affairs for granting us access to the data, and sincerely appreciate the help of Dr Nicole Pratt and her coauthors, who graciously shared their sequence symmetry analysis SAS program codes with us. In addition, we would like to acknowledge the kind contributions of the Australian Government Research Training Program Scholarship, the Curtin Health Innovation Research Institute (CHIRI) Biosciences Research Precinct Core Facility, and the School of Pharmacy and Biomedical Sciences (Curtin University) in supporting this research.…”