Sentinel Modular Program for Propensity Score-Matched Cohort Analyses: Application to Glyburide, Glipizide, and Serious Hypoglycemia

Application of the U.S. Food and Drug Administration's Sentinel Routine Querying Tools to the Taiwan Sentinel Data Model-formatted National Health Insurance Research Database

The U.S. Food and Drug Administration's Sentinel System is a leading distributed data network for drug safety surveillance in the world. The National Health Insurance Research Database (NHIRD) in Taiwan was converted into the Taiwan Sentinel Data Model (TSDM) based on the Sentinel Common Data Model (SCDM) version 6.0.2. The goal of this study was to investigate the feasibility of applying the same study designs, analytic choices, and analytic tools as used by the U.S. Sentinel System to examine the same drug-outcome associations in the TSDM-formatted NHIRD. Four known drug-outcome associations previously examined by the U.S. Sentinel System were selected as the use cases: (1) use of angiotensin-converting enzyme inhibitors (ACEIs) and risk of angioedema, (2) use of warfarin and risk of gastrointestinal bleeding, (3) use of oral clindamycin and risk of Clostridioides difficile infection (CDI), and (4) use of glyburide and risk of serious hypoglycemia. We followed the same study designs and analytic choices used by the U.S. Sentinel System and applied the Sentinel Routine Querying Tools to answer the same study questions within the TSDM-formatted NHIRD. The results showed that ACEIs were associated with a non-significant increase in risk of angioedema compared to beta-blockers (hazard ratio [HR]: 1.21; 95% confidence interval [CI]: 0.89-1.64); warfarin was associated with a higher risk of gastrointestinal bleeding compared to statins (HR: 1.72; 1.50-1.98); glyburide was associated with an increased risk of hypoglycemia compared to glipizide (HR: 1.61, 1.30-2.00). We were unable to evaluate the association between oral clindamycin and risk of CDI due to the low event number. Our study demonstrated that it was feasible to directly apply the publicly available Sentinel Routine Querying Tools within the TSDM-formatted NHIRD. However, sources of heterogeneity other than design and analytic differences should be carefully considered when comparing the results between the two systems.

Concepts of Designing and Implementing Pharmacoepidemiology Studies on the Safety of Systemic Treatments in Dermatology Practice

The U.S. Food and Drug Administration and clinical guidelines use evidence from pharmacoepidemiology studies to inform prescribing decisions and fill evidence gaps left by randomized controlled trials (RCTs). The long-term safety and infrequent adverse reactions are not well-understood when RCTs are short and involve few patients, as is the case for most systemic immunomodulating drugs in dermatology. A better understanding of the design and implementation of pharmacoepidemiology studies will help practitioners assess the accuracy of etiologic findings and use them with confidence in clinical practice. Conducting pharmacoepidemiology studies follows a structured approach, which we discuss in this article: (i) a design layer connects the research question with the appropriate study design, and considering which hypothetical RCT one ideally would want to conduct reduces inadvertent investigator errors; (ii) a measurement layer transforms longitudinal patient-level data into variables that identify the study population, patient characteristics, treatment, and outcomes; and (iii) the analysis focuses on the causal treatment effect estimation. The review and interpretation of pharmacoepidemiology studies should consider issues beyond a typical review of RCTs, chiefly the lack of baseline randomization and the use of secondary data. Well-designed and well-conducted pharmacoepidemiologic studies complement dermatology practice with critical information on prescribing systemic medications.

A new analytic tool for assessing the impact of the US Food and Drug Administration regulatory actions

PURPOSE

Develop and test a flexible, scalable tool using interrupted time series (ITS) analysis to assess the impact of Food and Drug Administration (FDA) regulatory actions on drug use.

METHODS

We applied the tool in the Sentinel Distributed Database to assess the impact of FDA's 2010 drug safety communications (DSC) concerning the safety of long-acting beta2-agonists (LABA) in adult asthma patients. We evaluated changes in LABA use by measuring the initiation of LABA alone and concomitant use of LABA and asthma controller medications (ACM) after the DSCs. The tool generated ITS graphs and used segmented regression to estimate baseline slope, level change, slope change, and absolute and relative changes at up to two user-specified time point (s) after the intervention. We tested the tool and compared our results against prior analyses that used similar measures.

RESULTS

Initiation of LABA alone declined among asthma patients aged 18-45 years before FDA DSCs (-0.10% per quarter; 95%CI: -0.11% to -0.09%) and the downward trend continued after. Concomitant use of LABA and ACM was stable before FDA DSCs. After FDA DSCs, there was a small trend decrease of 0.006% per quarter (95% CI, -0.008% to -0.003%). We found similar results among those aged 46-64 years and patients with poorly-controlled asthma. Our results were consistent with previous studies, confirming the performance of the new tool.

CONCLUSIONS

We developed and tested a reusable ITS tool in real-world databases formatted to the Sentinel Common Data Model that can assess the impact of regulatory actions on drug use.

Bioactivity-guided isolation of cyclooxygenase-2 inhibitors from Saussurea obvallata (DC.) Edgew. Using affinity solid phase extraction assay

ETHNOPHARMACOLOGICAL RELEVANCE

Saussurea obvallata (DC.) Edgew. is a traditional Tibetan medicine used for the treatment of inflammation-related diseases, but the scientific validation was very limited.

AIM OF THE STUDY

This study aimed to rapid screen and targeted isolate cyclooxygenase-2 (COX-2) inhibitors from S. obvallata extract.

MATERIALS AND METHODS

An efficient ligand-fishing method based on affinity solid phase extraction (A-SPE) combining with HPLC was developed. The identified COX-2 inhibitors were separated using preparative liquid chromatography. In vitro COX-2 inhibition assays were employed to confirm the inhibitory activities of the isolated compounds. In addition, the effect of the isolated compounds on the production of prostaglandin E2 (PGE2) and the expression of COX-2 in LPS-induced RAW 264.7 were evaluated.

RESULTS

A total of four phenylpropanoids, isolariciresinol, syringaresinol, pinoresinol and balanophonin were targeted isolated as COX-2 inhibitors with IC₅₀ values of 36.4 ± 2.6 μM, 23.1 ± 1.8 μM, 3.6 ± 0.3 μM and 12.1 ± 0.9 μM, respectively. The isolated compounds significantly inhibited LPS-induced NO production in a dose-dependent manner. And, the results of the inhibitory effect on the release of PGE₂ and the expression of COX-2 in LPS-induced macrophages were consistent with A-SPE analysis.

CONCLUSION

The present work demonstrated that the developed A-SPE-HPLC method could successfully targeted isolated COX-2 inhibitors from S. obvallata extract. And, the isolation results indicated that the therapeutic effect of S. obvallata on inflammation-related diseases was partly based on the COX-2 active ingredients.

Transparency of high-dimensional propensity score analyses: guidance for diagnostics and reporting

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.

Invited Commentary: Code Review-An Important Step Toward Reproducible Research

In this issue of the Journal, Vable et al. (Am J Epidemiol. 2021;190(10):2172-2177) discuss a systematic approach to code review as a way to improve reproducibility in epidemiologic research. Reproducibility needs to become a cornerstone of our work. In the present commentary, I discuss some of the implications of their proposal, other methods to reduce coding mistakes, and other methods to improve reproducibility in research in general. Finally, I discuss the fact that no one of these approaches is sufficient on its own; rather, these different steps need to become part of a culture that prioritizes reproducibility in research.

Conducting Real-world Evidence Studies on the Clinical Outcomes of Diabetes Treatments

Real-world evidence (RWE), the understanding of treatment effectiveness in clinical practice generated from longitudinal patient-level data from the routine operation of the healthcare system, is thought to complement evidence on the efficacy of medications from randomized controlled trials (RCTs). RWE studies follow a structured approach. (1) A design layer decides on the study design, which is driven by the study question and refined by a medically informed target population, patient-informed outcomes, and biologically informed effect windows. Imagining the randomized trial we would ideally perform before designing an RWE study in its likeness reduces bias; the new-user active comparator cohort design has proven useful in many RWE studies of diabetes treatments. (2) A measurement layer transforms the longitudinal patient-level data stream into variables that identify the study population, the pre-exposure patient characteristics, the treatment, and the treatment-emergent outcomes. Working with secondary data increases the measurement complexity compared to primary data collection that we find in most RCTs. (3) An analysis layer focuses on the causal treatment effect estimation. Propensity score analyses have gained in popularity to minimize confounding in healthcare database analyses. Well-understood investigator errors, like immortal time bias, adjustment for causal intermediates, or reverse causation, should be avoided. To increase reproducibility of RWE findings, studies require full implementation transparency. This article integrates state-of-the-art knowledge on how to conduct and review RWE studies on diabetes treatments to maximize study validity and ultimately increased confidence in RWE-based decision making.

Risk of Severe Abnormal Uterine Bleeding Associated with Rivaroxaban Compared with Apixaban, Dabigatran and Warfarin

INTRODUCTION

There have been reports of clinically relevant uterine bleeding events among women of reproductive age exposed to rivaroxaban.

OBJECTIVE

The aim of this study was to compare the risk of severe abnormal uterine bleeding (SAUB) resulting in transfusion or surgical intervention among women on rivaroxaban versus apixaban, dabigatran and warfarin.

METHODS

We conducted a retrospective cohort study in the FDA's Sentinel System (10/2010-09/2015) among females aged 18+ years with venous thromboembolism (VTE), or atrial flutter/fibrillation (AF) who newly initiated a direct oral anticoagulant (DOAC; rivaroxaban, apixaban, dabigatran) or warfarin. We followed women from dispensing date until the earliest of transfusion or surgery following vaginal bleeding, disenrollment, exposure or study end date, or recorded death. We estimated hazard ratios (HRs) using Cox proportional hazards regression via propensity score stratification. Four pairwise comparisons were conducted for each intervention.

RESULTS

Overall, there was an increased risk of surgical intervention with rivaroxaban when compared with dabigatran (HR 1.19; 95% CI 1.03-1.38), apixaban (1.23; 1.04-1.47), and warfarin (1.34; 1.22-1.47). No difference in risk for surgical intervention was observed for dabigatran-apixaban comparisons. Increased risk of transfusion was observed for rivaroxaban compared with dabigatran (1.49; 1.03-2.17) only. For patients with no gynecological history, rivaroxaban was associated with risk of surgical intervention compared with dabigatran (1.22; 1.05-1.42), apixaban (1.25; 1.04-1.49), and warfarin (1.36; 1.23-1.50).

CONCLUSION

Our study found increased SAUB risk with rivaroxaban use compared with other DOACs or warfarin. Increased risk with rivaroxaban was present among women without underlying gynecological conditions. Women on anticoagulant therapy should be aware of a risk of SAUB.

Risk of Psychiatric Adverse Events Among Montelukast Users

BACKGROUND

There have been conflicting results from observational studies regarding the risk of psychiatric adverse events (PAEs) with montelukast use.

OBJECTIVE

To determine whether there are associations of depressive disorders, self-harm, and suicide with use of montelukast compared with inhaled corticosteroid (ICS) use.

METHODS

Using data from the Sentinel Distributed Database from January 1, 2000, to September 30, 2015, patients (n = 457,377) exposed to montelukast or ICS, aged 6 years and older with a diagnosis of asthma, were matched 1:1 on propensity scores. Hazard ratios (HRs) and 95% CIs were estimated for each study outcome overall and by age, sex, psychiatric history, and pre-/post-2008 labeling updates using Cox proportional hazards regression models.

RESULTS

Exposure to montelukast was associated with a lower risk of treated outpatient depressive disorder (HR, 0.91; 95% CI, 0.89-0.93). No increased risks of inpatient depressive disorder (HR, 1.06; 95% CI, 0.90-1.24), self-harm (HR, 0.92; 95% CI, 0.69-1.21), or self-harm using a modified algorithm (HR, 0.81; 95% CI, 0.63-1.05) were observed with montelukast use compared with ICS use. Most PAEs occurred in the roughly one-third of patients having a past psychiatric history.

CONCLUSIONS

When compared with use of ICS, we did not find associations between montelukast use and hospitalizations for depression or self-harm events. Our findings should be interpreted considering the study's limitations. Psychiatric comorbidity was common, and most PAEs occurred in patients with a past psychiatric history.

Evaluation of the US Food and Drug Administration Sentinel Analysis Tools Using a Comparator with a Different Indication: Comparing the Rates of Gastrointestinal Bleeding in Warfarin and Statin Users

BACKGROUND

The US Food and Drug Administration's Sentinel System was established to monitor safety of regulated medical products. Sentinel investigators identified known associations between drugs and adverse events to test reusable analytic tools developed for Sentinel. This test case used a comparator with a different indication.

OBJECTIVE

We tested the ability of Sentinel's reusable analytic tools to identify the known association between warfarin and gastrointestinal bleeding (GIB). Statins, expected to have no effect on GIB, were the comparator. We further explored the impact of analytic features, including matching ratio and stratifying Cox regression analyses, on matched pairs.

METHODS

This evaluation included data from 14 Sentinel Data Partners. New users of warfarin and statins, aged 18 years and older, who had not received other anticoagulants or had recent GIB were matched on propensity score using 1:1 and 1:n variable ratio matching, matching statin users with warfarin users to estimate the average treatment effect in warfarin-treated patients. We compared the risk of GIB using Cox proportional hazards regression, following patients for the duration of their observed continuous treatment or until a GIB. For the 1:1 matched cohort, we conducted analyses with and without stratification on matched pair. The variable ratio matched cohort analysis was stratified on the matched set.

RESULTS

We identified 141,398 new users of warfarin and 2,275,694 new users of statins. In analyses stratified on matched pair/set, the hazard ratios (HR) for GIB in warfarin users compared with statin users were 2.78 (95% confidence interval [CI] 2.36-3.28) in the 1:1 matched cohort and 3.10 (95% CI 2.76-3.49) in the variable ratio matched cohort. The HR was lower in the analysis of the 1:1 matched cohort not stratified by matched pair (2.22, 95% CI 1.97-2.49), and highest early in treatment. Follow-up for warfarin users tended to be shorter than for statin users.

CONCLUSIONS

This study identified the expected GIB risk with warfarin compared with statins using an analytic tool developed for Sentinel. Our findings suggest that comparators with different indications may be useful in surveillance in select circumstances. Finally, in the presence of differential censoring, stratification by matched pair may reduce the potential for bias in Cox regression analyses.

Considerations for using distributed research networks to conduct aspects of randomized trials

Stakeholders in the clinical research enterprise are aligned around the need to make clinical research in general, and randomized controlled trials in particular, more meaningful and efficient. To that end, we have built distributed research networks (DRNs) for the Sentinel System, the National Institutes of Health (NIH) Collaboratory, and the National Patient-Centered Clinical Research Network (PCORnet). DRNs reuse electronic health record (EHR) and claims data for research. The design and use of health data DRNs is complicated by lack of uniformity in data collection, a fragmented healthcare system, and the imperative to protect research participants.

We describe the key elements of successful DRNs, as well as methods, challenges, and solutions we have encountered in using DRNs to support different phases of randomized, multi-site, clinical research. This work supports “real-world” efforts to build a learning health system and will enable others to conduct randomized clinical trial research using a DRN.

Reproducing Protocol-Based Studies Using Parameterizable Tools-Comparison of Analytic Approaches Used by Two Medical Product Surveillance Networks

The US Sentinel System and the Canadian Network for Observational Drug Effect Studies (CNODES) are two medical product safety surveillance networks. Using Sentinel's preprogrammed, parameterizable analytic tools, we reproduced two protocol-based studies conducted by CNODES to assess the risks of acute pancreatitis and heart failure (HF) associated with the use of incretin-based drugs, compared with use of ≥ 2 oral hypoglycemic agents. Results from the replication new-user cohort analyses aligned with those from the CNODES nested case-control studies. The adjusted hazard ratios were 0.95 (0.81-1.12; vs. 1.03 (0.87-1.22) in CNODES) for acute pancreatitis and 0.91 (0.84-1.00; vs. 0.82 (0.67-1.00) in CNODES) for HF among patients without HF history. The CNODES's common protocol approach allows studies tailored to specific safety questions, whereas the Sentinel's common data model plus pretested program approach enables more rapid analysis. Despite these differences, it is possible to obtain comparable results using both approaches.

Evaluating large-scale propensity score performance through real-world and synthetic data experiments

Background

Propensity score adjustment is a popular approach for confounding control in observational studies. Reliable frameworks are needed to determine relative propensity score performance in large-scale studies, and to establish optimal propensity score model selection methods.

Methods

We detail a propensity score evaluation framework that includes synthetic and real-world data experiments. Our synthetic experimental design extends the 'plasmode' framework and simulates survival data under known effect sizes, and our real-world experiments use a set of negative control outcomes with presumed null effect sizes. In reproductions of two published cohort studies, we compare two propensity score estimation methods that contrast in their model selection approach: L1-regularized regression that conducts a penalized likelihood regression, and the 'high-dimensional propensity score' (hdPS) that employs a univariate covariate screen. We evaluate methods on a range of outcome-dependent and outcome-independent metrics.

Results

L1-regularization propensity score methods achieve superior model fit, covariate balance and negative control bias reduction compared with the hdPS. Simulation results are mixed and fluctuate with simulation parameters, revealing a limitation of simulation under the proportional hazards framework. Including regularization with the hdPS reduces commonly reported non-convergence issues but has little effect on propensity score performance.

Conclusions

L1-regularization incorporates all covariates simultaneously into the propensity score model and offers propensity score performance superior to the hdPS marginal screen.

Evaluation of Switching Patterns in FDA's Sentinel System: A New Tool to Assess Generic Drugs

INTRODUCTION

Nearly 90% of drugs dispensed in the US are generic products.

OBJECTIVE

The aim of this study was to develop and implement a tool for analyzing manufacturer-level drug utilization and switching patterns within the US Food and Drug Administration's Sentinel system.

METHODS

A descriptive tool was designed to analyze data in the Sentinel common data model and was tested with two case studies-metoprolol extended release (ER) and lamotrigine ER-using claims data from four Sentinel data partners. We plotted initiators of each brand and generic product over time. For metoprolol ER, we evaluated rates of switching from generics around the time of manufacturing issues. For lamotrigine ER, we examined rates of switching back to the brand among those who switched from brand to generic.

RESULTS

We identified 1,651,285 initiators of metoprolol ER products between July 2008 and September 2015. We observed a large decrease in monthly metoprolol ER initiators (from 25,465 in December 2008 to 13,128 in February 2009), corresponding to recalls by generic manufacturers. We observed simultaneous increases in utilization of the authorized generic and brand products. We identified 4266 initiators of lamotrigine ER with an epilepsy diagnosis between January 2012 and September 2015. Among those who switched from brand to generic, the cumulative incidence of switching back was close to 20% at 2 years. Switchback rates were higher for the first available generic products.

CONCLUSIONS

This developed tool was able to elucidate novel utilization and switching patterns in two case studies. Such information can be used to support surveillance of generic drugs and biosimilars.

The Devil's in the details: Reports on reproducibility in pharmacoepidemiologic studies

PURPOSE

The U.S. Food and Drug Administration's Sentinel Initiative "modular programs" have been shown to replicate findings from conventional protocol-driven, custom-programmed studies. One such parallel assessment-dabigatran and warfarin and selected outcomes-produced concordant findings for three of four study outcomes. The effect estimates and confidence intervals for the fourth-acute myocardial infarction-had more variability as compared with other outcomes. This paper evaluates the potential sources of that variability that led to unexpected divergence in findings.

METHODS

We systematically compared the two studies and evaluated programming differences and their potential impact using a different dataset that allowed more granular data access for investigation. We reviewed the output at each of five main processing steps common in both study programs: cohort identification, propensity score estimation, propensity score matching, patient follow-up, and risk estimation.

RESULTS

Our findings point to several design features that warrant greater investigator attention when performing observational database studies: (a) treatment of recorded events (eg, diagnoses, procedures, and dispensings) co-occurring on the index date of study drug dispensing in cohort eligibility criteria and propensity score estimation and (b) construction of treatment episodes for study drugs of interest that have more complex dispensing patterns.

CONCLUSIONS

More precise and unambiguous operational definitions of all study parameters will increase transparency and reproducibility in observational database studies.

Data Mining for Adverse Drug Events With a Propensity Score-matched Tree-based Scan Statistic

The tree-based scan statistic is a statistical data mining tool that has been used for signal detection with a self-controlled design in vaccine safety studies. This disproportionality statistic adjusts for multiple testing in evaluation of thousands of potential adverse events. However, many drug safety questions are not well suited for self-controlled analysis. We propose a method that combines tree-based scan statistics with propensity score-matched analysis of new initiator cohorts, a robust design for investigations of drug safety. We conducted plasmode simulations to evaluate performance. In multiple realistic scenarios, tree-based scan statistics in cohorts that were propensity score matched to adjust for confounding outperformed tree-based scan statistics in unmatched cohorts. In scenarios where confounding moved point estimates away from the null, adjusted analyses recovered the prespecified type 1 error while unadjusted analyses inflated type 1 error. In scenarios where confounding moved point estimates toward the null, adjusted analyses preserved power, whereas unadjusted analyses greatly reduced power. Although complete adjustment of true confounders had the best performance, matching on a moderately mis-specified propensity score substantially improved type 1 error and power compared with no adjustment. When there was true elevation in risk of an adverse event, there were often co-occurring signals for clinically related concepts. TreeScan with propensity score matching shows promise as a method for screening and prioritization of potential adverse events. It should be followed by clinical review and safety studies specifically designed to quantify the magnitude of effect, with confounding control targeted to the outcome of interest.

How pharmacoepidemiology networks can manage distributed analyses to improve replicability and transparency and minimize bias

Several pharmacoepidemiology networks have been developed over the past decade that use a distributed approach, implementing the same analysis at multiple data sites, to preserve privacy and minimize data sharing. Distributed networks are efficient, by interrogating data on very large populations. The structure of these networks can also be leveraged to improve replicability, increase transparency, and reduce bias. We describe some features of distributed networks using, as examples, the Canadian Network for Observational Drug Effect Studies, the Sentinel System in the USA, and the European Research Network of Pharmacovigilance and Pharmacoepidemiology. Common protocols, analysis plans, and data models, with policies on amendments and protocol violations, are key features. These tools ensure that studies can be audited and repeated as necessary. Blinding and strict conflict of interest policies reduce the potential for bias in analyses and interpretation. These developments should improve the timeliness and accuracy of information used to support both clinical and regulatory decisions.

Association of Risk for Venous Thromboembolism With Use of Low-Dose Extended- and Continuous-Cycle Combined Oral Contraceptives: A Safety Study Using the Sentinel Distributed Database

IMPORTANCE

Continuous/extended cyclic estrogen use (84/7 or 365/0 days cycles) in combined oral contraceptives (COCs) could potentially expose women to an increased cumulative dose of estrogen, compared with traditional cyclic regimens (21/7 days cycle), and may increase the risk for venous thromboembolism (VTE).

OBJECTIVE

To determine, while holding the progestogen type constant, whether the risk for VTE is higher with use of continuous/extended COCs than with cyclic COCs among women who initiated a COC containing ethinyl estradiol and levonorgestrel.

DESIGN, SETTING, AND PARTICIPANTS

Incident user retrospective cohort study of primarily commercially insured US population identified from the Sentinel Distributed Database. Participants were women aged 18 to 50 years at the time of initiating a study COC between May 2007 and September 2015. Using a propensity score approach and Cox proportional hazards regression models, we estimated the hazard ratios of VTE overall and separately by ethinyl estradiol dose and age groups.

EXPOSURES

Initiation of continuous/extended or traditional cyclic COCs containing ethinyl estradiol or levonorgestrel of any dose.

MAIN OUTCOMES AND MEASURES

First VTE hospitalization that occurred during the study follow-up, identified by an inpatient International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis code of 415.1, 415.1x, 453, 453.x, or 453.xx.

RESULTS

We identified 210 691 initiators of continuous/extended COCs (mean [SD] age, 30.4 [8.6] years) and 522 316 initiators of cyclic COCs (mean [SD] age, 28.8 [8.3] years), with a mean of 0.7 person-years at risk among continuous/extended and cyclic users. Baseline cardiovascular and metabolic conditions (7.2% vs 4.7%), gynecological conditions (39.7% vs 32.3%), and health services utilization were slightly higher among continuous/extended cyclic than cyclic COC users. Propensity score matching decreased the hazard ratio estimates from 1.84 (95% CI, 1.53-2.21) to 1.32 (95% CI, 1.07-1.64) for continuous/extended use compared with cyclic COC use. The absolute risk difference (0.27 per 1000 persons) and the incidence rate difference (0.35 cases per 1000 person-years [1.44 vs 1.09 cases per 1000 person-years]) between the 2 propensity score-matched cohorts remained low, which may not translate into a clinically significant risk differences between cyclic and noncyclic estrogen use.

CONCLUSIONS AND RELEVANCE

Holding the progestogen type constant (levonorgestrel), we observed a slightly elevated VTE risk in association with continuous/extended COC use when compared with cyclic COC use. However, due to the small absolute risk difference and potential residual confounding, our findings did not show strong evidence supporting a VTE risk difference between continuous/extended and cyclic COC use.

Automated data-adaptive analytics for electronic healthcare data to study causal treatment effects

BACKGROUND

Decision makers in health care increasingly rely on nonrandomized database analyses to assess the effectiveness, safety, and value of medical products. Health care data scientists use data-adaptive approaches that automatically optimize confounding control to study causal treatment effects. This article summarizes relevant experiences and extensions.

METHODS

The literature was reviewed on the uses of high-dimensional propensity score (HDPS) and related approaches for health care database analyses, including methodological articles on their performance and improvement. Articles were grouped into applications, comparative performance studies, and statistical simulation experiments.

RESULTS

The HDPS algorithm has been referenced frequently with a variety of clinical applications and data sources from around the world. The appeal of HDPS for database research rests in 1) its superior performance in situations of unobserved confounding through proxy adjustment, 2) its predictable efficiency in extracting confounding information from a given data source, 3) its ability to automate estimation of causal treatment effects to the extent achievable in a given data source, and 4) its independence of data source and coding system. Extensions of the HDPS approach have focused on improving variable selection when exposure is sparse, using free text information and time-varying confounding adjustment.

CONCLUSION

Semiautomated and optimized confounding adjustment in health care database analyses has proven successful across a wide range of settings. Machine-learning extensions further automate its use in estimating causal treatment effects across a range of data scenarios.

A Query Workflow Design to Perform Automatable Distributed Regression Analysis in Large Distributed Data Networks

Introduction:

Patient privacy and data security concerns often limit the feasibility of pooling patient-level data from multiple sources for analysis. Distributed data networks (DDNs) that employ privacy-protecting analytical methods, such as distributed regression analysis (DRA), can mitigate these concerns. However, DRA is not routinely implemented in large DDNs.

Objective:

We describe the design and implementation of a process framework and query workflow that allow automatable DRA in real-world DDNs that use PopMedNet™, an open-source distributed networking software platform.

Methods:

We surveyed and catalogued existing hardware and software configurations at all data partners in the Sentinel System, a PopMedNet-driven DDN. Key guiding principles for the design included minimal disruptions to the current PopMedNet query workflow and minimal modifications to data partners’ hardware configurations and software requirements.

Results:

We developed and implemented a three-step process framework and PopMedNet query workflow that enables automatable DRA: 1) assembling a de-identified patient-level dataset at each data partner, 2) distributing a DRA package to data partners for local iterative analysis, and 3) iteratively transferring intermediate files between data partners and analysis center. The DRA query workflow is agnostic to statistical software, accommodates different regression models, and allows different levels of user-specified automation.

Discussion:

The process framework can be generalized to and the query workflow can be adopted by other PopMedNet-based DDNs.

Conclusion:

DRA has great potential to change the paradigm of data analysis in DDNs. Successful implementation of DRA in Sentinel will facilitate adoption of the analytic approach in other DDNs.

Comparative Safety of Sulfonylureas and the Risk of Sudden Cardiac Arrest and Ventricular Arrhythmia

OBJECTIVE

To examine the association between individual antidiabetic sulfonylureas and outpatient-originating sudden cardiac arrest and ventricular arrhythmia (SCA/VA).

RESEARCH DESIGN AND METHODS

We conducted a retrospective cohort study using 1999-2010 U.S. Medicaid claims from five large states. Exposures were determined by incident use of glyburide, glimepiride, or glipizide. Glipizide served as the reference exposure, as its effects are believed to be highly pancreas specific. Outcomes were ascertained by a validated ICD-9-based algorithm indicative of SCA/VA (positive predictive value ∼85%). Potential confounding was addressed by adjustment for multinomial high-dimensional propensity scores included as continuous variables in a Cox proportional hazards model.

RESULTS

Of sulfonylurea users under study (N = 519,272), 60.3% were female and 34.9% non-Hispanic Caucasian, and the median age was 58.0 years. In 176,889 person-years of sulfonylurea exposure, we identified 632 SCA/VA events (50.5% were immediately fatal) for a crude incidence rate of 3.6 per 1,000 person-years. Compared with glipizide, propensity score-adjusted hazard ratios for SCA/VA were 0.82 (95% CI 0.69-0.98) for glyburide and 1.10 (0.89-1.36) for glimepiride. Numerous secondary analyses showed a very similar effect estimate for glyburide; yet, not all CIs excluded the null.

CONCLUSIONS

Glyburide may be associated with a lower risk of SCA/VA than glipizide, consistent with a very small clinical trial suggesting that glyburide may reduce ventricular tachycardia and isolated ventricular premature complexes. This potential benefit must be contextualized by considering putative effects of different sulfonylureas on other cardiovascular end points, cerebrovascular end points, all-cause death, and hypoglycemia.

Evaluation of the US Food and Drug Administration sentinel analysis tools in confirming previously observed drug-outcome associations: The case of clindamycin and Clostridium difficile infection

PURPOSE

The Food and Drug Administration's Sentinel System developed parameterized, reusable analytic programs for evaluation of medical product safety. Research on outpatient antibiotic exposures, and Clostridium difficile infection (CDI) with non-user reference groups led us to expect a higher rate of CDI among outpatient clindamycin users vs penicillin users. We evaluated the ability of the Cohort Identification and Descriptive Analysis and Propensity Score Matching tools to identify a higher rate of CDI among clindamycin users.

METHODS

We matched new users of outpatient dispensings of oral clindamycin or penicillin from 13 Data Partners 1:1 on propensity score and followed them for up to 60 days for development of CDI. We used Cox proportional hazards regression stratified by Data Partner and matched pair to compare CDI incidence.

RESULTS

Propensity score models at 3 Data Partners had convergence warnings and a limited range of predicted values. We excluded these Data Partners despite adequate covariate balance after matching. From the 10 Data Partners where these models converged without warnings, we identified 807 919 new clindamycin users and 8 815 441 new penicillin users eligible for the analysis. The stratified analysis of 807 769 matched pairs included 840 events among clindamycin users and 290 among penicillin users (hazard ratio 2.90, 95% confidence interval 2.53, 3.31).

CONCLUSIONS

This evaluation produced an expected result and identified several potential enhancements to the Propensity Score Matching tool. This study has important limitations. CDI risk may have been related to factors other than the inherent properties of the drugs, such as duration of use or subsequent exposures.

Development and application of two semi-automated tools for targeted medical product surveillance in a distributed data network

PURPOSE OF REVIEW

An important component of the Food and Drug Administration's Sentinel Initiative is the active post-market risk identification and analysis (ARIA) system, which utilizes semi-automated, parameterized computer programs to implement propensity-score adjusted and self-controlled risk interval designs to conduct targeted surveillance of medical products in the Sentinel Distributed Database. In this manuscript, we review literature relevant to the development of these programs and describe their application within the Sentinel Initiative.

RECENT FINDINGS

These quality-checked and publicly available tools have been successfully used to conduct rapid, replicable, and targeted safety analyses of several medical products. In addition to speed and reproducibility, use of semi-automated tools allows investigators to focus on decisions regarding key methodological parameters. We also identified challenges associated with the use of these methods in distributed and prospective datasets like the Sentinel Distributed Database, namely uncertainty regarding the optimal approach to estimating propensity scores in dynamic data among data partners of heterogeneous size.

SUMMARY

Future research should focus on the methodological challenges raised by these applications as well as developing new modular programs for targeted surveillance of medical products.

Comparative risk of serious hypoglycemia with oral antidiabetic monotherapy: A retrospective cohort study

PURPOSE

To examine and compare risks of serious hypoglycemia among antidiabetic monotherapy-treated adults receiving metformin, a sulfonylurea, a meglitinide, or a thiazolidinedione.

METHODS

We performed a retrospective cohort study of apparently new users of monotherapy with metformin, glimepiride, glipizide, glyburide, pioglitazone, rosiglitazone, nateglinide, or repaglinide within a dataset of Medicaid beneficiaries from California, Florida, New York, Ohio, and Pennsylvania. We did not include users of dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 agonists, or sodium-glucose co-transporter 2 inhibitors. We identified serious hypoglycemia outcomes within 180 days following new use using a validated, diagnosis-based algorithm. We calculated age- and sex-standardized outcome occurrence rates for each drug and generated propensity score-adjusted hazard ratios vs metformin using Cox proportional hazards regression.

RESULTS

The ranking of standardized occurrence rates of serious hypoglycemia was glyburide > glimepiride > glipizide > repaglinide > nateglinide > rosiglitazone > pioglitazone > metformin. Rates were increased for all study drugs at higher average daily doses. Adjusted hazard ratios (95% confidence intervals) vs metformin were 3.95 (3.66-4.26) for glyburide, 3.28 (2.98-3.62) for glimepiride, 2.57 (2.38-2.78) for glipizide, 2.03 (1.64-2.52) for repaglinide, 1.21 (0.89-1.66) for nateglinide, 0.90 (0.75-1.07) for rosiglitazone, and 0.80 (0.68-0.93) for pioglitazone.

CONCLUSIONS

Sulfonylureas were associated with the highest rates of serious hypoglycemia. Among all study drugs, the highest rate was seen with glyburide. Pioglitazone was associated with a lower adjusted hazard for serious hypoglycemia vs metformin, while rosiglitazone and nateglinide had hazards similar to that of metformin.