Comparative-effectiveness research (CER) aims to produce actionable evidence regarding the effectiveness and safety of medical products and interventions as they are used outside of controlled research settings. Although CER evidence regarding medications is particularly needed shortly after market approval, key methodological challenges include (i) potential bias due to channeling of patients to the newly marketed medication because of various patient-, physician-, and system-related factors; (ii) rapid changes in the characteristics of the user population during the early phase of marketing; and (iii) lack of timely data and the often small number of users in the first few months of marketing. We propose a mix of approaches to generate comparative-effectiveness data in the early marketing period, including sequential cohort monitoring with secondary health-care data and propensity score (PS) balancing, as well as extended follow-up of phase III and phase IV trials, indirect comparisons of placebo-controlled trials, and modeling and simulation of virtual trials.

A drug‐drug interaction (DDI) occurs when one or more drugs affect the pharmacokinetics (the body's effect on the drug) and/or pharmacodynamics (the drug's effect on the body) of one or more other drugs. Pharmacoepidemiologic studies are the principal way of studying the health effects of potential DDIs. This article discusses aspects of pharmacoepidemiologic research designs that are particularly salient to the design and interpretation of pharmacoepidemiologic studies of DDIs.

We examined variation in fracture rates among antidepressant initiators identified from Medicare data in two US states and assessed whether observed variation was explained by affinity for serotonin transport receptors. We used Cox-proportional hazards models to compare fracture rates of the hip, humerus, pelvis, wrist, and a composite of these, among propensity-score matched cohorts of users of secondary amine tricyclics (2° TCAs), tertiary amine tricyclics (3° TCAs), selective serotonin reuptake inhibitors (SSRIs), and atypical antidepressants. As compared to 2° TCA initiators, SSRIs initiators had the highest composite fracture rate (hazard ratio [HR], 1.30; 95% confidence interval [CI] 1.12–1.52) followed by atypical antidepressants (HR, 1.12; 95% CI 0.96–1.31) and 3° TCAs (HR, 1.01; 95% CI 0.87–1.18). Results were robust to sensitivity analyses. While SSRI use was associated with the highest rate of fractures, variation in fracture risk across specific antidepressant medications did not depend on affinity for serotonin transport receptors.

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A11at market entry of each drug of interest and using a sequential propensity score matched cohort design. We applied four BRA metrics: number needed to treat and number needed to harm (NNT|NNH); incremental net benefit (INB) with maximum acceptable risk [MAR], INB with relative-value adjusted life years [RVALYs], and INB with quality-adjusted life years [QALYs]. We determined whether and when the bootstrapped 99% confidence interval (CI) for each metric excluded zero, indicating net favorability of one drug over the other. Results: For rofecoxib, all four metrics yielded a negative value, suggesting net favorability of ns-NSAIDs over rofecoxib, and the 99% CI for all but the NNT|NNH excluded the null during follow-up. For prasugrel, only the 99% CI for INB-QALY excluded the null, but trends in values over time were similar across the four metrics, suggesting overall net favorability of prasugrel versus clopidogrel. The 99% CI for INB-RVALY and INB-QALY excluded the null in the denosumab example, suggesting net favorability of denosumab over bisphosphonates. ConClusions: Prospective benefit-risk monitoring can be used to determine net favorability of a new drug in electronic healthcare data. In three examples, existing BRA metrics produced qualitatively similar results but differed with respect to alert generation. INB-QALY produced the most conclusive findings across the three examples.

A11at market entry of each drug of interest and using a sequential propensity score matched cohort design. We applied four BRA metrics: number needed to treat and number needed to harm (NNT|NNH); incremental net benefit (INB) with maximum acceptable risk [MAR], INB with relative-value adjusted life years [RVALYs], and INB with quality-adjusted life years [QALYs]. We determined whether and when the bootstrapped 99% confidence interval (CI) for each metric excluded zero, indicating net favorability of one drug over the other. Results: For rofecoxib, all four metrics yielded a negative value, suggesting net favorability of ns-NSAIDs over rofecoxib, and the 99% CI for all but the NNT|NNH excluded the null during follow-up. For prasugrel, only the 99% CI for INB-QALY excluded the null, but trends in values over time were similar across the four metrics, suggesting overall net favorability of prasugrel versus clopidogrel. The 99% CI for INB-RVALY and INB-QALY excluded the null in the denosumab example, suggesting net favorability of denosumab over bisphosphonates. ConClusions: Prospective benefit-risk monitoring can be used to determine net favorability of a new drug in electronic healthcare data. In three examples, existing BRA metrics produced qualitatively similar results but differed with respect to alert generation. INB-QALY produced the most conclusive findings across the three examples.

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