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Abstract Objectives While tocilizumab may increase serum lipid levels, recent studies do not suggest a link between tocilizumab use and clinical cardiovascular risk in patients with rheumatoid arthritis (RA). Methods To compare cardiovascular safety of tocilizumab with abatacept, we conducted a cohort study using data from Medicare (2010–2013), IMS PharMetrics (2011–2014) and MarketScan (2011-6/2015). RA patients aged ≥18 years who newly started tocilizumab or abatacept entered the cohort on the day of their first use of tocilizumab or abatacept after a continuous enrollment period for ≥365 days. The primary outcome was a composite cardiovascular endpoint of hospitalization for myocardial infarction or stroke. To control for more than 60 confounders, tocilizumab starters were propensity score (PS)-matched to abatacept starters with a variable ratio of 1:3 within each database. A fixed-effects model combined database-specific hazard ratios (HR). Results We included 6237 tocilizumab starters PS-matched to 14,685 abatacept starters in all three databases. Mean age was 72 years in Medicare, 51 in PharMetrics and 53 in MarketScan. The incidence rate of the composite cardiovascular events per 100 person-years ranged from 0.37 (PharMetrics) to 1.64 (Medicare) in the tocilizumab group and from 0.59 (PharMetrics) to 1.69 (Medicare) in the abatacept group. The risk of the composite cardiovascular events was similar between the two groups across all three databases, with a combined HR of 0.82 (95% CI: 0.55–1.22) in tocilizumab versus abatacept starters. Conclusions This multi-database cohort study found no difference in the risk of cardiovascular events in RA patients who newly started tocilizumab versus abatacept. [ABSTRACT FROM AUTHOR]

Background: Healthcare providers are increasingly encouraged to improve their patients' adherence to chronic disease medications. Prediction of adherence can identify patients in need of intervention, but most prediction efforts have focused on claims data, which may be unavailable to providers. Electronic health records (EHR) are readily available and may provide richer information with which to predict adherence than is currently available through claims.Methods: In a linked database of complete Medicare Advantage claims and comprehensive EHR from a multi-specialty outpatient practice, we identified patients who filled a prescription for a statin, antihypertensive, or oral antidiabetic during 2011 to 2012. We followed patients to identify subsequent medication filling patterns and used group-based trajectory models to assign patients to adherence trajectories. We then identified potential predictors from both claims and EHR data and fit a series of models to evaluate the accuracy of each data source in predicting medication adherence.Results: Claims were highly predictive of patients in the worst adherence trajectory (C=0.78), but EHR data also provided good predictions (C=0.72). Among claims predictors, presence of a prior gap in filling of at least 6 days was by far the most influential predictor. In contrast, good predictions from EHR data required complex models with many variables.Conclusion: EHR data can provide good predictions of adherence trajectory and therefore may be useful for providers seeking to deploy resource-intensive interventions. However, prior adherence information derived from claims is most predictive, and can supplement EHR data when it is available. [ABSTRACT FROM AUTHOR]

Objectives: To compare benefit-risk assessment (BRA) methods for determining whether and when sufficient evidence exists to indicate that one drug is favorable over another in prospective monitoring.Methods: We simulated prospective monitoring of a new drug (A) versus an alternative drug (B) with respect to two beneficial and three harmful outcomes. We generated data for 1000 iterations of six scenarios and applied four BRA metrics: number needed to treat and number needed to harm (NNT|NNH), incremental net benefit (INB) with maximum acceptable risk, INB with relative-value-adjusted life-years, and INB with quality-adjusted life-years. We determined the proportion of iterations in which the 99% confidence interval for each metric included and excluded the null and we calculated mean time to alerting.Results: With no true difference in any outcome between drugs A and B, the proportion of iterations including the null was lowest for INB with relative-value-adjusted life-years (64%) and highest for INB with quality-adjusted life-years (76%). When drug A was more effective and the drugs were equally safe, all metrics indicated net favorability of A in more than 70% of the iterations. When drug A was safer than drug B, NNT|NNH had the highest proportion of iterations indicating net favorability of drug A (65%). Mean time to alerting was similar among methods across the six scenarios.Conclusions: BRA metrics can be useful for identifying net favorability when applied to prospective monitoring of a new drug versus an alternative drug. INB-based approaches similarly outperform unweighted NNT|NNH approaches. Time to alerting was similar across approaches. [ABSTRACT FROM AUTHOR]

Background: Benefit-risk assessment (BRA) methods can combine measures of benefits and risks into a single value.Objectives: To examine BRA metrics for prospective monitoring of new drugs in electronic health care data.Methods: Using two electronic health care databases, we emulated prospective monitoring of three drugs (rofecoxib vs. nonselective nonsteroidal anti-inflammatory drugs, prasugrel vs. clopidogrel, and denosumab vs. bisphosphonates) using a sequential propensity score-matched cohort design. We applied four BRA metrics: number needed to treat and number needed to harm; incremental net benefit (INB) with maximum acceptable risk; INB with relative-value-adjusted life-years; 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 nonselective nonsteroidal anti-inflammatory drugs over rofecoxib, and the 99% CI for all but the number needed to treat and number needed to harm 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-relative-value-adjusted life-years 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 health care data. In three examples, existing BRA metrics produced qualitatively similar results but differed with respect to alert generation. [ABSTRACT FROM AUTHOR]