Real‐world on‐treatment and initial treatment absolute risk differences for dabigatran vs warfarin in older US adults

Abstract

PurposeTrials and past observational work compared dabigatran and warfarin in patients with atrial fibrillation, but few reported estimates of absolute harm and benefit under real‐world adherence patterns, particularly in older adults that may have differing benefit‐harm profiles. We aimed to estimate risk differences for ischemic stroke, death, and gastrointestinal bleeding after initiating dabigatran and warfarin in older adults (a) when patients adhere to treatment and (b) under real‐world adherence patterns.MethodsIn a 20% sample of nationwide Medicare claims from 2010 to 2015, we identified beneficiaries aged 66 years and older initiating warfarin and dabigatran. We followed individuals from initiation until death or October 2015 (initial treatment, IT) and separately censored individuals' follow‐up after drug switches and gaps in supply (on‐treatment, OT). We applied inverse probability of treatment and standardized morbidity ratio weights, as well as inverse probability of censoring weights, to estimate two‐year risk differences (RDs) for dabigatran vs warfarin.ResultsWe identified 10,717 dabigatran and 74,891 warfarin initiators. Weighted OT RDs suggested decreased ischemic stroke risk for dabigatran vs warfarin; IT RDs indicated increased or no change in ischemic stroke risk. Regardless of follow‐up approach and weighting strategy, risk of death appeared lower and risk of gastrointestinal bleeding appeared higher when comparing dabigatran vs warfarin.ConclusionsDabigatran use was associated with lower risks of mortality and ischemic stroke in routine care when older adults stayed on treatment. IT analyses suggested that these benefits may be diminished under real‐world patterns of switching and discontinuation.

An Overview of Current Methods for Real-world Applications to Generalize or Transport Clinical Trial Findings to Target Populations of Interest

“…They found that missingness in the target population that was not MCAR could introduce bias whereas missingness present only in the trial sample presented less of a concern. 65 Based on simulation results by us and others, [66][67][68][69] we recommend implementing a MI-passive approach (estimate PS after imputing underlying variables to estimate PS) along with a PSI-within approach for integrating imputation results (conduct IPSW within each imputed dataset), and additionally include trial indicator (for both trial and target populations), and treatment and outcome variables (among trial participants) in the imputation model. Alternatively, we also recommend coupling multiple imputation with bootstrapping methods to estimate the uncertainty of the treatment effect as exemplified in Mollan et al 62 More work is needed to fully explore the impact of missingness on translating findings and remains an open topic for research.…”

Performance of a Claims-Based Frailty Proxy Using Varying Frailty Ascertainment Lookback Windows

“…8,9 It has been used extensively for population stratification and confounding control in studies using Medicare data. [10][11][12][13][14] While the original model was developed using an 8-month frailty ascertainment window, 8 subsequent research studies have employed different durations. For example, Zhang et al used a 6-month frailty ascertainment window to control for confounding by frailty when assessing the effectiveness of influenza vaccines in older adults.…”

“…Prior studies have demonstrated that the Faurot frailty index is strongly associated with short-term geriatric outcomes, including mortality, skilled nursing facility (SNF) admission, and hospitalization 8,9 . It has been used extensively for population stratification and confounding control in studies using Medicare data 10–14 . While the original model was developed using an 8-month frailty ascertainment window, 8 subsequent research studies have employed different durations.…”