Jessica M. Franklin

Introduction In clinical trials of patients with atopic dermatitis (AD), conjunctivitis and keratitis occurred more frequently with dupilumab than placebo. Studies using real-world data have also shown a higher incidence with dupilumab but did not use validated algorithms to identify the population and outcomes. The objective of this study was to investigate the incidence of conjunctivitis and keratitis among patients with moderate-to-severe AD treated with dupilumab relative to dupilumab-naïve patients in a real-world setting using validated algorithms. Methods A retrospective observational cohort study was conducted in an insurance claims database using validated algorithms for moderate-to-severe AD and ocular outcomes. Initiators of dupilumab were identified and propensity score (PS) matched with dupilumab-naïve patients with moderate-to-severe AD. Incidence rate ratios (IRRs) with 95% confidence intervals (CIs) for conjunctivitis and keratitis during follow-up were estimated using Poisson regression. Results Among 13,790 patients in the moderate-to-severe AD study population, 2175 dupilumab initiators and 2189 dupilumab-naïve patients were included in the analysis. Dupilumab was associated with an increased risk of conjunctivitis (IRR = 1.86 [95% CI 1.51–2.30]) and keratitis (IRR = 4.06 [95% CI 1.70–9.68]) compared to patients with moderate-to-severe AD not receiving dupilumab. The cumulative 1-year risk of conjunctivitis was 15.8% and 8.4% in the dupilumab and dupilumab-naïve cohorts, respectively; the cumulative 1-year risk of keratitis was 1.3% and 0.2%, respectively. Conclusion Study findings are consistent with safety data from clinical trials and existing literature. However, a few keratitis events were observed, and post hoc analyses suggested residual confounding might be present. The known benefit-risk profile for dupilumab remains unchanged.

Background: Ocular surface disorders have been reported among patients with various medical conditions, including atopic dermatitis (AD). Nonetheless, validated algorithms to identify conjunctivitis and keratitis in claims data are lacking.Objective: Develop validated, claims‐based algorithms for conjunctivitis and keratitis among patients with AD using medical records.Methods: Patients with AD were identified in a claims database between March 2017 and November 2019. Among these patients, candidate algorithms were developed that included diagnosis codes for conjunctivitis or keratitis, alone and in combination with ophthalmic treatments. Among patients who met ≥ 1 candidate algorithms, a subset was randomly selected for medical record review. Additionally, records from a random sample of patients with AD were reviewed to assess sensitivity. Overall, 341 records were sought and 262 adjudicated by an expert ophthalmologist. The positive predictive value (PPV) of each algorithm was calculated and compared to a pre‐specified threshold of ≥ 70%.Results: For conjunctivitis, the final algorithm was ≥ 1 conjunctivitis diagnosis (PPV = 81%, 95% confidence interval [CI]: 73%–87%). For keratitis, the final algorithm combined the following 2 candidate algorithms: ≥ 1 keratitis diagnosis and ≥ 1 dispensing of a topical antibiotic or antibiotic–steroid combination (PPV = 91%); and ≥ 1 keratitis diagnosis and ≥ 1 dispensing of an ophthalmic corticosteroid, topical immune‐modulator, or topical NSAID (PPV = 68%) for an overall PPV of 80% (95% CI: 55%–93%).Conclusion: The first claims‐based algorithms to identify conjunctivitis and keratitis among AD patients were developed and validated. They are available for use in future studies, particularly to better understand conjunctivitis and keratitis occurrence among patients with AD.

Purpose: The high‐dimensional propensity score (HDPS) is a semi‐automated procedure for confounder identification, prioritisation and adjustment in large healthcare databases that requires investigators to specify data dimensions, prioritisation strategy and tuning parameters. In practice, reporting of these decisions is inconsistent and this can undermine the transparency, and reproducibility of results obtained. We illustrate reporting tools, graphical displays and sensitivity analyses to increase transparency and facilitate evaluation of the robustness of analyses involving HDPS.Methods: Using a study from the UK Clinical Practice Research Datalink that implemented HDPS we demonstrate the application of the proposed recommendations.Results: We identify seven considerations surrounding the implementation of HDPS, such as the identification of data dimensions, method for code prioritisation and number of variables selected. Graphical diagnostic tools include assessing the balance of key confounders before and after adjusting for empirically selected HDPS covariates and the identification of potentially influential covariates. Sensitivity analyses include varying the number of covariates selected and assessing the impact of covariates behaving empirically as instrumental variables. In our example, results were robust to both the number of covariates selected and the inclusion of potentially influential covariates. Furthermore, our HDPS models achieved good balance in key confounders.Conclusions: The data‐adaptive approach of HDPS and the resulting benefits have led to its popularity as a method for confounder adjustment in pharmacoepidemiological studies. Reporting of HDPS analyses in practice may be improved by the considerations and tools proposed here to increase the transparency and reproducibility of study results.

The randomized controlled trial (RCT) is the gold standard for evaluating the causal effects of medications. Limitations of RCTs have led to increasing interest in using real‐world evidence (RWE) to augment RCT evidence and inform decision making on medications. Although RWE can be either randomized or nonrandomized, nonrandomized RWE can capitalize on the recent proliferation of large healthcare databases and can often answer questions that cannot be answered in randomized studies due to resource constraints. However, the results of nonrandomized studies are much more likely to be impacted by confounding bias, and the existence of unmeasured confounders can never be completely ruled out. Furthermore, nonrandomized studies require more complex design considerations which can sometimes result in design‐related biases. We discuss questions that can help investigators or evidence consumers evaluate the potential impact of confounding or other biases on their findings: Does the design emulate a hypothetical randomized trial design? Is the comparator or control condition appropriate? Does the primary analysis adjust for measured confounders? Do sensitivity analyses quantify the potential impact of residual confounding? Are methods open to inspection and (if possible) replication? Designing a high‐quality nonrandomized study of medications remains challenging and requires broad expertise across a range of disciplines, including relevant clinical areas, epidemiology, and biostatistics. The questions posed in this paper provide a guiding framework for assessing the credibility of nonrandomized RWE and could be applied across many clinical questions.

There is increasing interest in utilizing real‐world data (RWD) to produce real‐world evidence (RWE) on the benefits and risks of medical products that could support regulatory approval decisions. The field of pharmacoepidemiology has a long history of focusing on data and evidence that would now be termed “real‐world,” including evidence from healthcare claims, registries, and electronic health records. However, several emerging trends over the past decade are converging to support the use of these and other RWD sources for approval decisions, and there are several recent examples and ongoing research that demonstrate how RWE may be used to support regulatory approval of new or expanded indications. The goal of this article is to review the current landscape and future directions of the use of RWE in this context. This manuscript is endorsed by the International Society for Pharmacoepidemiology (ISPE).

The emergence and global spread of the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) has resulted in an urgent need for evidence on medical interventions and outcomes of the resulting disease, coronavirus disease 2019 (COVID‐19). Although many randomized controlled trials (RCTs) evaluating treatments and vaccines for COVID‐19 are already in progress, the number of clinical questions of interest greatly outpaces the available resources to conduct RCTs. Therefore, there is growing interest in whether nonrandomized real‐world evidence (RWE) can be used to supplement RCT evidence and aid in clinical decision making, but concerns about nonrandomized RWE have been highlighted by a proliferation of RWE studies on medications and COVID‐19 outcomes with widely varying conclusions. The objective of this paper is to review some clinical questions of interest, potential data types, challenges, and merits of RWE in COVID‐19, resulting in recommendations for nonrandomized RWE designs and analyses based on established RWE principles.

Purpose: To evaluate recent trends in inpatient postoperative utilization of opioid and non‐opioid analgesics in US hospitals.Methods: Using Premier Research database (October 2007–September 2017), we identified adults who were hospitalized for inpatient surgical procedures (N = 6 068 133). For each month, we calculated proportion of patients admitted that month who were administered (1) opioids, (2) acetaminophen, (3) non‐steroidal anti‐inflammatory drugs (NSADs), and (4) gabapentinoids (gabapentin or pregabalin) during the postoperative period, defined as inpatient postoperative days 1–7, unless discharged earlier. For patients administered opioids, we estimated total and average daily postoperative opioid dose in morphine milligram equivalents (MMEs). Monthly measures were standardized to the distribution of surgeries and the length of postoperative stay within each surgery during the last year of available data.Results: Overall, 90.8% of patients were administered opioids postoperatively; mean total postoperative dose was 304 MMEs (median 130). Both the frequency and the amount of opioids administered remained stable over 2007–2017. Postoperative use of acetaminophen increased from mean standardized monthly prevalence of 78% in 2007–2008 to 85% in 2017, while the use of NSAIDs remained stable at a standardized mean of 37%. The use of gabapentinoids increased from below 10% in 2007–2008 to the mean standardized monthly prevalence of 23% in 2017.Conclusion: Despite growing awareness of risks associated with postoperative opioid use, we observed no change in postoperative utilization of opioids in US hospitals. Increasing the use of non‐opioid pain management approaches could constitute an important target in our efforts to curtail US opioid epidemic.

Background: Regulators are evaluating the use of non-interventional real-world evidence (RWE) studies to assess the effectiveness of medical products. The RCT-DUPLICATE initiative uses a structured process to design RWE studies emulating randomized controlled trials (RCTs) and compare results. Here, we report findings of the first 10 trial emulations, evaluating cardiovascular outcomes of antidiabetic or antiplatelet medications. Methods: We selected 3 active-controlled and 7 placebo-controlled RCTs for replication. Using patient-level claims data from US commercial and Medicare payers, we implemented inclusion/exclusion criteria, selected primary endpoints, and comparator populations to emulate those of each corresponding RCT. Within the trial-mimicking populations, we conducted propensity score matching to control for >120 pre-exposure confounders. All study parameters were prospectively defined and protocols registered before hazard ratios (HRs) and 95% confidence intervals (CIs) were computed. Success criteria for the primary analysis were pre-specified for each replication. Results: Despite attempts to emulate RCT design as closely as possible, differences between the RCT and corresponding RWE study populations remained. The regulatory conclusions were equivalent in 6 of 10. The RWE emulations achieved a HR estimate that was within the 95% CI from the corresponding RCT in 8 of 10 studies. In 9 of 10, either the regulatory or estimate agreement success criteria were fulfilled. The largest differences in effect estimates were found for RCTs where second-generation sulfonylureas were used as a proxy for placebo regarding cardiovascular effects. Nine of 10 replications had a standardized difference between effect estimates of Conclusions: Agreement between RCT and RWE findings varies depending on which agreement metric is used. Interim findings indicate that selection of active comparator therapies with similar indications and use patterns enhances the validity of RWE. Even in the context of active comparators, concordance between RCT and RWE findings is not guaranteed, partially because trials are not emulated exactly. More trial emulations are needed to understand how often and in what contexts RWE findings match RCTs. Clinical Trial Registration: URL: https://clinicaltrials.gov Unique Identifiers: NCT03936049, NCT04215523, NCT04215536, NCT03936010, NCT03936036, NCT03936062, NCT03936023, NCT03648424, NCT04237935, NCT04237922

Coarsened exact matching (CEM) is a matching method proposed as an alternative to other techniques commonly used to control confounding. We compared CEM with 3 techniques that have been used in pharmacoepidemiology: propensity score matching, Mahalanobis distance matching, and fine stratification by propensity score (FS). We evaluated confounding control and effect-estimate precision using insurance claims data from the Pharmaceutical Assistance Contract for the Elderly (1999–2002) and Medicaid Analytic eXtract (2000–2007) databases (United States) and from simulated claims-based cohorts. CEM generally achieved the best covariate balance. However, it often led to high bias and low precision of the risk ratio due to extreme losses in study size and numbers of outcomes (i.e., sparse data bias)—especially with larger covariate sets. FS usually was optimal with respect to bias and precision and always created good covariate balance. Propensity score matching usually performed almost as well as FS, especially with higher index exposure prevalence. The performance of Mahalanobis distance matching was relatively poor. These findings suggest that CEM, although it achieves good covariate balance, might not be optimal for large claims-database studies with rich covariate information; it might be ideal if only a few (<10) strong confounders must be controlled.