ImportanceNonrandomized studies using insurance claims databases can be analyzed to produce real-world evidence on the effectiveness of medical products. Given the lack of baseline randomization and measurement issues, concerns exist about whether such studies produce unbiased treatment effect estimates.ObjectiveTo emulate the design of 30 completed and 2 ongoing randomized clinical trials (RCTs) of medications with database studies using observational analogues of the RCT design parameters (population, intervention, comparator, outcome, time [PICOT]) and to quantify agreement in RCT-database study pairs.Design, Setting, and ParticipantsNew-user cohort studies with propensity score matching using 3 US claims databases (Optum Clinformatics, MarketScan, and Medicare). Inclusion-exclusion criteria for each database study were prespecified to emulate the corresponding RCT. RCTs were explicitly selected based on feasibility, including power, key confounders, and end points more likely to be emulated with real-world data. All 32 protocols were registered on ClinicalTrials.gov before conducting analyses. Emulations were conducted from 2017 through 2022.ExposuresTherapies for multiple clinical conditions were included.Main Outcomes and MeasuresDatabase study emulations focused on the primary outcome of the corresponding RCT. Findings of database studies were compared with RCTs using predefined metrics, including Pearson correlation coefficients and binary metrics based on statistical significance agreement, estimate agreement, and standardized difference.ResultsIn these highly selected RCTs, the overall observed agreement between the RCT and the database emulation results was a Pearson correlation of 0.82 (95% CI, 0.64-0.91), with 75% meeting statistical significance, 66% estimate agreement, and 75% standardized difference agreement. In a post hoc analysis limited to 16 RCTs with closer emulation of trial design and measurements, concordance was higher (Pearson r, 0.93; 95% CI, 0.79-0.97; 94% meeting statistical significance, 88% estimate agreement, 88% standardized difference agreement). Weaker concordance occurred among 16 RCTs for which close emulation of certain design elements that define the research question (PICOT) with data from insurance claims was not possible (Pearson r, 0.53; 95% CI, 0.00-0.83; 56% meeting statistical significance, 50% estimate agreement, 69% standardized difference agreement).Conclusions and RelevanceReal-world evidence studies can reach similar conclusions as RCTs when design and measurements can be closely emulated, but this may be difficult to achieve. Concordance in results varied depending on the agreement metric. Emulation differences, chance, and residual confounding can contribute to divergence in results and are difficult to disentangle.

Background Treatment decisions for Coronavirus Disease 2019 (COVID-19) depend on disease severity, but the prescribing pattern by severity and drivers of therapeutic choices remain unclear. Objectives The objectives of the study were to evaluate pharmacological treatment patterns by COVID-19 severity and identify the determinants of prescribing for COVID-19. Methods Using electronic health record data from a large Massachusetts-based healthcare system, we identified all patients aged ≥ 18 years hospitalized with laboratory-confirmed COVID-19 from 1 March to 24 May, 2020. We defined five levels of COVID-19 severity at hospital admission: (1) hospitalized but not requiring supplemental oxygen; (2–4) hospitalized and requiring oxygen ≤ 2, 3–4, and ≥ 5 L per minute, respectively; and (5) intubated or admitted to an intensive care unit. We assessed the medications used to treat COVID-19 or as supportive care during hospitalization. Results Among 2821 patients hospitalized for COVID-19, we found inpatient mortality increased by severity from 5% for level 1 to 23% for level 5. As compared to patients with severity level 1, those with severity level 5 were 3.53 times (95% confidence interval 2.73–4.57) more likely to receive a medication used to treat COVID-19. Other predictors of treatment were fever, low oxygen saturation, presence of co-morbidities, and elevated inflammatory biomarkers. The use of most COVID-19 relevant medications has dropped substantially while the use of remdesivir and therapeutic anticoagulants has increased over the study period. Conclusions Careful consideration of disease severity and other determinants of COVID-19 drug use is necessary for appropriate conduct and interpretation of non-randomized studies evaluating outcomes of COVID-19 treatments. Electronic supplementary material The online version of this article (10.1007/s40265-020-01424-7) contains supplementary material, which is available to authorized users.

IMPORTANCE Guidelines for managing venous thromboembolism (VTE) recommend at least 90 days of therapy with oral anticoagulants. Limited evidence exists about the optimal drug for continuing therapy beyond 90 days.OBJECTIVE To compare having prescriptions dispensed for apixaban, rivaroxaban, or warfarin after an initial 90 days of anticoagulation therapy for the outcomes of hospitalization for recurrent VTE, major bleeding, and death. DESIGN, SETTING, AND PARTICIPANTSThis exploratory retrospective cohort study used data from fee-for-service Medicare (2009Medicare ( -2017 and from 2 commercial health insurance (2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) databases and included 64 642 adults who initiated oral anticoagulation following hospitalization discharge for VTE and continued treatment beyond 90 days.EXPOSURES Apixaban, rivaroxaban, or warfarin prescribed after an initial 90-day treatment for VTE. MAIN OUTCOMES AND MEASURESPrimary outcomes included hospitalization for recurrent VTE and hospitalization for major bleeding. Analyses were adjusted using propensity score weighting. Patients were followed up from the end of the initial 90-day treatment episode until treatment cessation, outcome, death, disenrollment, or end of available data. Weighted Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% CIs. RESULTSThe study included 9167 patients prescribed apixaban (mean [SD] age, 71 [14] years; 5491 [59.9%] women), 12 468 patients prescribed rivaroxaban (mean [SD] age, 69 [14] years; 7067 [56.7%] women), and 43 007 patients prescribed warfarin (mean [SD] age, 70 [15] years; 25 404 [59.1%] women). The median (IQR) follow-up was 109 (59-228) days for recurrent days for major bleeding outcome. After propensity score weighting, the incidence rate of hospitalization for recurrent VTE was significantly lower for apixaban compared with warfarin (9.8 vs 13.5 per 1000 person-years; HR, 0.69 [95% CI, 0.49-0.99]), but the incidence rates were not significantly different between apixaban and rivaroxaban (9.8 vs 11.6 per 1000 person-years; HR, 0.80 [95% CI, 0.53-1.19]) or rivaroxaban and warfarin (HR, 0.87 [95% CI, 0.65-1.16]). Rates of hospitalization for major bleeding were 44.4 per 1000 person-years for apixaban, 50.0 per 1000 person-years for rivaroxaban, and 47.1 per 1000 person-years for warfarin, yielding HRs of 0.92 (95% CI, 0.78-1.09) for apixaban vs warfarin, 0.86 (95% CI, 0.71-1.04) for apixaban vs rivaroxaban, and 1.07 (95% CI, 0.93-1.24) for rivaroxaban vs warfarin. CONCLUSIONS AND RELEVANCEIn this exploratory analysis of patients prescribed extended-duration oral anticoagulation therapy after hospitalization for VTE, prescription dispenses for apixaban beyond 90 days, compared with warfarin beyond 90 days, were significantly associated with a modestly lower rate of hospitalization for recurrent VTE, but no significant difference in rate of hospitalization for major bleeding. There were no significant differences for comparisons of apixa...