OBJECTIVE: The objective of this study was to compare treatment persistence and rates of seizure-related events in patients who initiate antiepileptic drug (AED) therapy with a generic versus a brand-name product.
METHODS: We used linked electronic medical and pharmacy claims data to identify Medicare beneficiaries who initiated one of five AEDs (clonazepam, gabapentin, oxcarbazepine, phenytoin, zonisamide). We matched initiators of generic versus brand-name versions of these drugs using a propensity score that accounted for demographic, clinical, and health service utilization variables. We used a Cox proportional hazards model to compare rates of seizure-related emergency room (ER) visit or hospitalization (primary outcome) and ER visit for bone fracture or head injury (secondary outcome) between the matched generic and brand-name initiators. We also compared treatment persistence, measured as time to first 14-day treatment gap, between generic and brand-name initiators.
RESULTS: We identified 19,760 AED initiators who met study eligibility criteria; 18,306 (93%) initiated a generic AED. In the matched cohort, we observed 47 seizure-related hospitalizations and ER visits among brand-name initiators and 31 events among generic initiators, corresponding to a hazard ratio of 0.53 (95% confidence interval, 0.30 to 0.96). Similar results were observed for the secondary clinical endpoint and across sensitivity analyses. Mean time to first treatment gap was 124.2 days (standard deviation [sd], 125.8) for brand-name initiators and 137.9 (sd, 148.6) for generic initiators.
SIGNIFICANCE: Patients who initiated generic AEDs had fewer adverse seizure-related clinical outcomes and longer continuous treatment periods before experiencing a gap than those who initiated brand-name versions.
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.
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.
INTRODUCTION: Several small studies have reported inconsistent findings about the safety of selective serotonin reuptake inhibitors (SSRIs) among patients undergoing coronary artery bypass grafting (CABG). We sought to investigate post-CABG bleeding and mortality outcomes related to antidepressant exposure.
METHODS: We identified patients who underwent CABG between 2004 and 2008 in the Premier Perspective Comparative Database. We determined whether they received SSRIs, other antidepressants, or no antidepressants on any pre-CABG hospital day and used Cox proportional hazards models to compare bleeding and mortality rates among the exposure groups while adjusting for potential confounders based on administrative data, pre-CABG charge codes, and discharge diagnosis codes.
RESULTS: We identified 132,686 eligible patients: 7112 exposed to SSRIs, 1905 exposed to other antidepressants, and 123,668 unexposed. As compared with no exposure, neither SSRIs (hazard ratio [HR] 0.98; 95 % confidence interval [CI] 0.90-1.07) nor other antidepressants (HR 1.11; 95 % CI 0.96-1.28) increased major bleeds, and neither SSRIs (HR 0.93; 95 % CI 0.80-1.07) nor other antidepressants (HR 0.84; 95 % CI 0.62-1.14) increased mortality. Both SSRIs (HR 1.14; 95 % CI 1.10-1.18) and other antidepressants (HR 1.11; 95 % CI 1.03-1.19) were associated with a slight increase in receipt of one or more packed red blood cell (pRBC) units, but neither were associated with substantial increases in receipt of three or more pRBC units (HR 1.06; 95 % CI 0.96-1.17 for SSRIs; HR 1.09; 95 % CI 0.91-1.31 for other antidepressants).
CONCLUSION: In this large cohort study, neither SSRIs nor other antidepressants were associated with elevated rates of major bleed, or in-hospital mortality.
Thiazolidinediones, a class of medications indicated for the treatment of type 2 diabetes mellitus, reduce inflammation and have been shown to provide a therapeutic benefit in animal models of Parkinson disease. We examined the association between treatment with thiazolidinediones and the onset of Parkinson disease in older individuals. We performed a cohort study of 29,397 Medicare patients enrolled in state pharmaceutical benefits programs who initiated treatment with thiazolidinediones or sulfonylureas during the years 1997 through 2005 and had no prior diagnosis of Parkinson disease. New users of thiazolidinediones were propensity score matched to new users of sulfonylureas and followed to determine whether they were diagnosed with Parkinson disease. We used Cox proportional hazards models to compare time to diagnosis of Parkinson disease in the propensity score-matched populations. To assess the association with duration of use, we performed several analyses that required longer continuous use of medications. In the primary analysis, thiazolidinedione users had a hazard ratio for a diagnosis of Parkinson disease of 1.09 (95% confidence interval: 0.71, 1.66) when compared with sulfonylurea users. Increasing the duration-of-use requirements to 10 months did not substantially change the association; the hazard ratios ranged from 1.00 (95% confidence interval: 0.49, 2.05) to 1.17 (95% confidence interval: 0.60, 2.25). Thiazolidinedione use was not associated with a longer time to diagnosis of Parkinson disease than was sulfonylurea use, regardless of duration of exposure.
BACKGROUND: Patients with rare diseases experience the health care system differently than patients with more common conditions. They can therefore provide important perspectives on the process of developing therapeutics for their conditions.
METHODS: We conducted three in-person focus groups involving rare disease patients (n = 9), caregivers (n = 8), and advocates (n = 9). Focus group participants were asked to describe their experiences with a rare disease, what they would want to know about a new drug for the disease, what outcomes they believe should be assessed in drug testing, perceptions of off-label use of a drug for treating a rare disease, views on participation in clinical trials, and opinions of the US Food and Drug Administration's (FDA's) function. The coding structure was populated from the transcripts of the sessions, using Atlas.ti qualitative analysis software, and then analyzed for common themes.
RESULTS: Participants described the challenges of learning to live with a poorly understood condition for which treatment is limited. Rare disease patients were willing to accept certain risks in their care in the hopes of finding some benefit, but also expressed frustrations with the costs of their care and the lack of scientific data about their treatments. They were concerned that the development and testing of therapies should, as quickly as possible, yield effective treatments to advance their quality of life.
CONCLUSION: With limited therapeutic options, rare disease patients often considered off-label treatments or novel drugs that posed substantial risk. Nonetheless, rare disease patients generally appreciated the rigor of the research underlying the drugs and supported the FDA's gatekeeping role.
BACKGROUND: Solid clinical evidence supports the effectiveness and safety of multiple drugs in treating diabetes, dyslipidemia, and hypertension, and numerous fixed-dose combination products (FDCs) containing such drugs have been developed for patients with more severe forms of these diseases. We sought to evaluate the extent to which utilization of treatment combinations for these conditions corresponded to the availability of FDCs.
METHODS: Using claims data from a large national commercial insurer, we identified 2 cohorts of patients: those who filled multiple single-agent drugs to treat diabetes, dyslipidemia, and hypertension in 2012, and those who used FDCs containing these products during the same period. We determined the fill rate of single-agent pairs and FDCs, availability of FDCs for the most frequently filled single-agent and drug class pairs, and the number of conditions treated by frequently filled single-agent pairs and FDCs.
RESULTS: During our study period, 848,082 patients filled prescriptions for 3,248 unique single-agent pairs (mean 4.7 per patient, standard deviation [SD] 5.0); and 568,923 patients received prescriptions for 43 unique FDCs (mean 1.1 per patient, SD 0.3). Three (15%) of the 20 most frequently filled single-agent pairs were available as FDCs, whereas 9 (45%) of the 20 most frequently filled drug class pairs were available as FDCs. Nearly all of the frequently filled FDCs had lower fill rates than the most frequently filled single-agent pairs.
CONCLUSIONS: Utilization of drug combinations to treat cardiovascular conditions does not correspond well with availability of FDCs containing these agents. A concerted set of strategies should be implemented to streamline the development of useful combination products, including expedited approval pathways and increased investment in formulation studies.
BACKGROUND: Generic drugs are approved on the basis of pharmaceutical equivalence and bioequivalence. Some drug products have unique structural or functional attributes, necessitating modified approaches to bioequivalence determinations.
OBJECTIVE: The aim of this systematic review was to identify studies that evaluated laboratory or clinical outcomes of six drugs approved via modified bioequivalence approaches.
DATA SOURCES: We conducted a systematic review of articles published through February 2014 in MEDLINE, EMBASE, and International Pharmaceutical Abstracts related to six recent drugs subject to modified regulatory approaches: venlafaxine extended release tablet (Effexor XR), acarbose (Precose), enoxaparin (Lovenox), vancomycin capsules (Vancocin), sodium ferric gluconate (Ferrlecit), and calcitonin salmon nasal spray (Miacalcin NS). We included all empirical evaluations (whether in vivo or in vitro) and excluded case studies, qualitative analyses, and pharmacoeconomic evaluations. Studies were summarized and evaluated on their methodological quality and assessed for bias using the Cochrane Risk of Bias Assessment Tool. Articles were divided into studies of US FDA-approved generics and non-FDA-approved generics available in non-US locations.
DATA EXTRACTION: We extracted drug(s) studied, study design, setting, sample size, population characteristics, study endpoints and results, and source of funding.
DATA SYNTHESIS: After retrieving 1408 articles and searching through the full text of 106 articles, we found 26 articles that met our inclusion criteria-8 examining FDA-approved versions and 18 examining non-FDA-approved versions. Among FDA-approved generics, five studies of enoxaparin showed minor variations in biologic activities of unclear clinical importance, and no publications involved acarbose, venlafaxine ER, or vancomycin capsules. Among non-FDA-approved generics, nine studies of enoxaparin supported generic bioequivalence, despite three showing minor variations in drug activity. Four of six studies of venlafaxine ER supported generic bioequivalence, while two found a lack of bioequivalence with a Canadian generic version of the drug. Most studies were either highly susceptible to bias (12/26) or were not able to be assessed for bias (13/26), in part because eight studies were abstracts/posters without full reports.
CONCLUSIONS: Pharmaceutical manufacturers sometimes raise scientific concerns related to potential generic versions of their drugs; however, in the six cases we reviewed, these companies did not follow up the pre-approval concerns they raised with any methodologically rigorous post-approval testing using clinical endpoints. Despite their pre-approval controversy, experience with these generic drugs provides reassurance of their clinical interchangeability. Systematized post-approval study of certain generic drug bioequivalence determinations is needed.
Generic drugs possessing the same active ingredients, dosage form, strength, route of administration, and labeling can be approved by the US Food and Drug Administration (FDA) as interchangeable with a brand-name drug without needing to repeat the formal Phase I, II, and III clinical trials conducted by the original manufacturers. In recent years, the FDA has approved several generic drugs using product-specific testing to determine therapeutic equivalence in accordance with the unique features of the particular drug. These have been used in two primary situations: (1) cases for which certain bioequivalence studies were not relevant; and (2) cases of complex molecules that may require specially tailored pharmaceutical equivalence studies. Examples include venlafaxine extended release, acarbose, vancomycin capsules, sodium ferric gluconate, salmon calcitonin nasal spray, and enoxaparin. Product-specific approaches to demonstrating therapeutic equivalence are essential to avoid delays in low-cost generic drug availability but can have important clinical implications; yet, currently, there is no formal process in place to monitor the safety and effectiveness of generic drugs approved using modified regulatory pathways. Several strategies can be used to monitor the safety and effectiveness of generic drugs approved via product-specific determinations of therapeutic equivalence.
BACKGROUND: Patients, physicians, and other decision makers make implicit but inevitable trade-offs among risks and benefits of treatments. Many methods have been proposed to promote transparent and rigorous benefit-risk analysis (BRA).
OBJECTIVE: To propose a framework for classifying BRA methods on the basis of key factors that matter most for patients by using a common mathematical notation and compare their results using a hypothetical example.
METHODS: We classified the available BRA methods into three categories: 1) unweighted metrics, which use only probabilities of benefits and risks; 2) metrics that incorporate preference weights and that account for the impact and duration of benefits and risks; and 3) metrics that incorporate weights based on decision makers' opinions. We used two hypothetical antiplatelet drugs (a and b) to compare the BRA methods within our proposed framework.
RESULTS: Unweighted metrics include the number needed to treat and the number needed to harm. Metrics that incorporate preference weights include those that use maximum acceptable risk, those that use relative-value-adjusted life-years, and those that use quality-adjusted life-years. Metrics that use decision makers' weights include the multicriteria decision analysis, the benefit-less-risk analysis, Boers' 3 by 3 table, the Gail/NCI method, and the transparent uniform risk benefit overview. Most BRA methods can be derived as a special case of a generalized formula in which some are mathematically identical. Numerical comparison of methods highlights potential differences in BRA results and their interpretation.
CONCLUSIONS: The proposed framework provides a unified, patient-centered approach to BRA methods classification based on the types of weights that are used across existing methods, a key differentiating feature.
JoshuaJGagne3⃣The problem occurs only with truly persistent users -- i.e., those who will continue to be exposed until they die -- and not with long-term exposures as long as a steady-state of starting and stopping is reached. See box plot in 1⃣ for unbiased fixed 180-day exposure estimate.
JoshuaJGagne1⃣Bias is small but apparent when 10% of patients are persistent users (see figure: biased odds ratio = 1.11; true OR = 1.00). Bias is sizable (OR = 1.43) when 30% of patients are persistent users and it grows quickly from there. t.co/r0QXhXLvk0