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OBJECTIVE: We compared health care resource utilization (HCRU) and costs for inpatient and outpatient services and dispensed medications in older adults with type 2 diabetes initiating empagliflozin versus dipeptidyl peptidase 4 inhibitors (DPP-4is). RESEARCH DESIGN AND METHODS: The study population included U.S. Medicare fee-for-service beneficiaries with diabetes (age ≥65 years) initiating empagliflozin or DPP-4is (August 2014 to September 2018). We estimated rate ratios (RRs) for HCRU outcomes using zero-inflated negative binomial regression and per-member per-year (PMPY) cost differences using generalized linear model with gamma distributions, overall and stratified by baseline cardiovascular disease (CVD), after adjusting for 143 baseline covariates using 1:1 propensity score matching. RESULTS: We identified 23,335 matched pairs (mean age 72 years, 51% with baseline CVD). HCRU rates were lower in empagliflozin versus DPP-4i initiators (number of inpatient days, RR 0.89 [95% CI 0.82, 0.97]; number of emergency department [ED] visits, 0.86 [0.82, 0.91]; number of hospitalizations, 0.86 [0.79, 0.93]; number of office visits, 0.96 [0.95, 0.98]). Inpatient cost (−$713 PMPY [95% CI −847, −579), outpatient cost (−$198 PMPY [−272, −124]), and total cost of care (−$1,109 PMPY [−1,478, −739]) were lower for empagliflozin versus DPP-4is, although diabetes medication cost was higher in empagliflozin initiators ($454 PMPY [95% CI 284, 567]). In the CVD subgroup, total cost was lower for empagliflozin initiators (−$2,005 PMPY [−2,451, −1,337]), while the difference was attenuated in the non-CVD subgroup (−$296 PMPY [−740, 148]). CONCLUSIONS: Among older adults with diabetes, empagliflozin was associated with a lower number of inpatient days, hospitalizations, ED visits, and office visits and with lower costs of care compared with DPP-4is, especially in those with history of CVD. [ABSTRACT FROM AUTHOR]

Almost all external control arm analyses to contextualize findings of a single arm trial struggle with two key issues: the lack of baseline randomization, and equally important, the difference in data collection between the experimental arm with its primary data collection, and the external control arm using secondary data. We illustrate the data calibration design to remedy issues arising from differential measurements in the two arms, and discuss the hybrid design that expands an underpowered randomized internal control arm with real‐world data to mitigate the lack of randomization of the external control arm. We show how the two approaches fit into an evidence‐development strategy that naturally builds on the incremental insights gained. [ABSTRACT FROM AUTHOR]

Aim: Non‐randomized studies on bariatric surgery have reported large reductions in mortality within 6‐12 months after surgery compared with non‐surgical patients. It is unclear whether these findings are the result of bias. Study Design and Setting: We searched PubMed to identify all non‐randomized studies investigating the effect of bariatric surgery on all‐cause mortality compared with non‐surgical patients. We assessed these studies for potential confounding and time‐related biases. We conducted bias analyses to quantify the effect of these biases. Results: We identified 21 cohort studies that met our inclusion criteria. Among those, 11 were affected by immortal time bias resulting from the misclassification or exclusion of relevant follow‐up time. Five studies were subject to potential confounding bias because of a lack of adjustment for body mass index (BMI). All studies used an inadequate comparator group that lacked indications for bariatric surgery. Bias analyses to correct for potential confounding from BMI shifted the effect estimates towards the null [reported hazard ratio (HR): 0.78 vs. bias‐adjusted HR: 0.92]. Bias analyses to correct for the presence of immortal time also shifted the effect estimates towards the null (adjustment for 2‐year wait time: reported HR: 0.57 vs. bias‐adjusted HR: 0.81). Conclusion: Several important sources of bias were identified in non‐randomized studies of the effectiveness of bariatric surgery versus non‐surgical comparators on mortality. Future studies should ensure that confounding by BMI is accounted for, considering the choice of the comparator group, and that the design or analysis avoids immortal time bias from the misclassification or exclusion. [ABSTRACT FROM AUTHOR]

Purpose: There is increasing recognition of the importance of transparency and reproducibility in scientific research. This study aimed to quantify the extent to which programming code is publicly shared in pharmacoepidemiology, and to develop a set of recommendations on this topic. Methods: We conducted a literature review identifying all studies published in Pharmacoepidemiology and Drug Safety (PDS) between 2017 and 2022. Data were extracted on the frequency and types of programming code shared, and other key open science practices (clinical codelist sharing, data sharing, study preregistration, and stated use of reporting guidelines and preprinting). We developed six recommendations for investigators who choose to share code and gathered feedback from members of the International Society for Pharmacoepidemiology (ISPE). Results: Programming code sharing by articles published in PDS ranged from 1.8% in 2017 to 9.5% in 2022. It was more prevalent among articles with a methodological focus, simulation studies, and papers which also shared record‐level data. Conclusion: Programming code sharing is rare but increasing in pharmacoepidemiology studies published in PDS. We recommend improved reporting of whether code is shared and how available code can be accessed. When sharing programming code, we recommend the use of permanent digital identifiers, appropriate licenses, and, where possible, adherence to good software practices around the provision of metadata and documentation, computational reproducibility, and data privacy. [ABSTRACT FROM AUTHOR]

Introduction: While randomized controlled trials remain the reference standard for evaluating treatment efficacy, there is an increased interest in the use of external control arms (ECA), namely in oncology, using real-world data (RWD). Challenges related to measurement of real-world oncology endpoints, like progression-free survival (PFS), are one factor limiting the use and acceptance of ECAs as comparators to trial populations. Differences in how and when disease assessments occur in the real-world may introduce measurement error and limit the comparability of real-world PFS (rwPFS) to trial progression-free survival. While measurement error is a known challenge when conducting an externally-controlled trial with real-world data, there is limited literature describing key contributing factors, particularly in the context of multiple myeloma (MM). Methods: We distinguish between biases attributed to how endpoints are derived or ascertained (misclassification bias) and when outcomes are observed or assessed (surveillance bias). We further describe how misclassification of progression events (i.e., false positives, false negatives) and irregular assessment frequencies in multiple myeloma RWD can contribute to these biases, respectively. We conduct a simulation study to illustrate how these biases may behave, both individually and together. Results: We observe in simulation that certain types of measurement error may have more substantial impacts on comparability between mismeasured median PFS (mPFS) and true mPFS than others. For instance, when the observed progression events are misclassified as either false positives or false negatives, mismeasured mPFS may be biased towards earlier (mPFS bias = -6.4 months) or later times (mPFS bias = 13 months), respectively. However, when events are correctly classified but assessment frequencies are irregular, mismeasured mPFS is more similar to the true mPFS (mPFS bias = 0.67 months). Discussion: When misclassified progression events and irregular assessment times occur simultaneously, they may generate bias that is greater than the sum of their parts. Improved understanding of endpoint measurement error and how resulting biases manifest in RWD is important to the robust construction of ECAs in oncology and beyond. Simulations that quantify the impact of measurement error can help when planning for ECA studies and can contextualize results in the presence of endpoint measurement differences. [ABSTRACT FROM AUTHOR]