Ross, Joseph S

Background/aims There has been growing interest in better understanding the potential of observational research methods in medical product evaluation and regulatory decision-making. Previously, we used linked claims and electronic health record data to emulate two ongoing randomized controlled trials, characterizing the populations and results of each randomized controlled trial prior to publication of its results. Here, our objective was to compare the populations and results from the emulated trials with those of the now-published randomized controlled trials. Methods This study compared participants’ demographic and clinical characteristics and study results between the emulated trials, which used structured data from OptumLabs Data Warehouse, and the published PRONOUNCE and GRADE trials. First, we examined the feasibility of implementing the baseline participant characteristics included in the published PRONOUNCE and GRADE trials’ using real-world data and classified each variable as ascertainable, partially ascertainable, or not ascertainable. Second, we compared the emulated trials and published randomized controlled trials for baseline patient characteristics (concordance determined using standardized mean differences <0.20) and results of the primary and secondary endpoints (concordance determined by direction of effect estimates and statistical significance). Results The PRONOUNCE trial enrolled 544 participants, and the emulated trial included 2226 propensity score-matched participants. In the PRONOUNCE trial publication, one of the 32 baseline participant characteristics was listed as an exclusion criterion on ClinicalTrials.gov but was ultimately not used. Among the remaining 31 characteristics, 9 (29.0%) were ascertainable, 11 (35.5%) were partially ascertainable, and 10 (32.2%) were not ascertainable using structured data from OptumLabs. For one additional variable, the PRONOUNCE trial did not provide sufficient detail to allow its ascertainment. Of the nine variables that were ascertainable, values in the emulated trial and published randomized controlled trial were discordant for 6 (66.7%). The primary endpoint of time from randomization to the first major adverse cardiovascular event and secondary endpoints of nonfatal myocardial infarction and stroke were concordant between the emulated trial and published randomized controlled trial. The GRADE trial enrolled 5047 participants, and the emulated trial included 7540 participants. In the GRADE trial publication, 8 of 34 (23.5%) baseline participant characteristics were ascertainable, 14 (41.2%) were partially ascertainable, and 11 (32.4%) were not ascertainable using structured data from OptumLabs. For one variable, the GRADE trial did not provide sufficient detail to allow for ascertainment. Of the eight variables that were ascertainable, values in the emulated trial and published randomized controlled trial were discordant for 4 (50.0%). The primary endpoint of time to hemoglobin A1c ≥7.0% was mostly concordant between the emulated trial and the published randomized controlled trial. Conclusion Despite challenges, observational methods and real-world data can be leveraged in certain important situations for a more timely evaluation of drug effectiveness and safety in more diverse and representative patient populations.

Background: Physicians’ professional networks are an important source of new medical information and have been shown to influence the adoption of new treatments, but it is unknown how physician networks impact the de-adoption of harmful practices. Methods: We analyzed changes in physicians’ use of dronedarone after the PALLAS trial (Palbociclib Collaborative Adjuvant Study; November 2011) showed that dronedarone increased the risk of death from cardiovascular events among patients with permanent atrial fibrillation. Deidentified administrative claims from the OptumLabs Data Warehouse were combined with physicians’ demographic information from the Doximity database and publicly available data on physicians’ patient-sharing relationships compiled by the Centers for Medicare and Medicaid Services. We used a linear probability model with an interrupted linear time trend specification to model the impact of the PALLAS trial on physicians’ dronedarone usage between 2009 and 2014. Results: Before the PALLAS trial, the use of dronedarone was increasing by 0.22 percentage points per quarter (95% CI, 0.19–0.25) in our Medicare Advantage sample (N=343 429 patient-quarter observations) and 0.63 percentage points per quarter (95% CI, 0.52–0.75) in our commercially insured sample (N=44 402 patient-quarter observations). After the PALLAS trial and subsequent United States Food and Drug Administration black box warning, physicians in the Medicare Advantage sample with an above-median number of network connections to other physicians decreased their quarterly usage of dronedarone by 0.12 percentage points more per quarter (95% CI, −0.20 to −0.04; P =0.031) than physicians with equal to or below the median number of network connections. Similar patterns existed in the commercially insured sample ( P =0.0318). Conclusions: After controlling for a wide range of patient, physician, and geographic characteristics, physicians with a greater number of network connections were faster de-adopters of dronedarone for patients with permanent atrial fibrillation after the PALLAS trial and subsequent United States Food and Drug Administration black box warning detailed the harmfulness of dronedarone for these patients. Policies for improving physicians’ responsiveness to new medical information should consider utilizing the influence of these important professional network relationships.

Background/Aims: The Pediatric Research Equity Act and Best Pharmaceuticals for Children Act are intended to promote the conduct of clinical trials that generate pediatric-specific evidence about drug safety and efficacy. This study assesses the quality of evidence generated through Pediatric Research Equity Act–mandated and Best Pharmaceuticals for Children Act–incentivized clinical trials of hematology/oncology drugs and characterizes subsequent changes in pediatric drug utilization rates. Methods: Trial characteristics (blinding, randomization, and comparator group) were determined for clinical trials that supported pediatric label changes. Using data from OptumLabs ® Data Warehouse, a de-identified administrative claims database, we calculated pediatric utilization rates for each drug. We calculated monthly utilization rates from January 2003 (or from the first month in which data were available) to December 2018. Results: We identified 11 hematology/oncology drugs that underwent pediatric label changes under the Pediatric Research Equity Act Pediatric Research Equity Act and/or Best Pharmaceuticals for Children Act, and we identified 15 trials supporting these changes. Of these trials, 36% (5/14) were randomized, 31% (4/13) were blinded, and 36% (5/14) used a comparator group. A median of 49 children (interquartile range 29.5) received the drug under investigation across these trials. Pediatric label changes were not associated with subsequent changes in pediatric drug utilization. Although some drugs saw increased pediatric use after gaining new pediatric indications, this pattern was not consistently observed. In addition, there was no evidence to suggest that drugs were utilized less frequently after they failed to receive pediatric indications. Conclusions: Clinical trials of hematology/oncology drugs conducted under the Pediatric Research Equity Act Pediatric Research Equity Act and Best Pharmaceuticals for Children Act generally have low methodological rigor, and the resulting label changes are not consistently associated with changes in pediatric utilization. Alternative regulatory strategies and study designs may be necessary to maximize the impact of newly generated knowledge on drug utilization.