Mixed Approach Retrospective Analyses of Suicide and Suicidal Ideation for Brand Compared with Generic Central Nervous System Drugs.

INTRODUCTION: Several different types of drugs acting on the central nervous system (CNS) have previously been associated with an increased risk of suicide and suicidal ideation (broadly referred to as suicide). However, a differential association between brand and generic CNS drugs and suicide has not been reported. OBJECTIVES: This study compares suicide adverse event rates for brand versus generic CNS drugs using multiple sources of data. METHODS: Selected examples of CNS drugs (sertraline, gabapentin, zolpidem, and methylphenidate) were evaluated via the US FDA Adverse Event Reporting System (FAERS) for a hypothesis-generating study, and then via administrative claims and electronic health record (EHR) data for a more rigorous retrospective cohort study. Disproportionality analyses with reporting odds ratios and 95% confidence intervals (CIs) were used in the FAERS analyses to quantify the association between each drug and reported suicide. For the cohort studies, Cox proportional hazards models were used, controlling for demographic and clinical characteristics as well as the background risk of suicide in the insured population. RESULTS: The FAERS analyses found significantly lower suicide reporting rates for brands compared with generics for all four studied products (Breslow-Day P < 0.05). In the claims- and EHR-based cohort study, the adjusted hazard ratio (HR) was statistically significant only for sertraline (HR 0.58; 95% CI 0.38-0.88). CONCLUSION: Suicide reporting rates were disproportionately larger for generic than for brand CNS drugs in FAERS and adjusted retrospective cohort analyses remained significant only for sertraline. However, even for sertraline, temporal confounding related to the close proximity of black box warnings and generic availability is possible. Additional analyses in larger data sources with additional drugs are needed.

Duke Scholars

Author David Page Biostatistics & Bioinformatics, Division of Biostatistics