A protocol for active surveillance of acute myocardial infarction in association with the use of a new antidiabetic pharmaceutical agent

The diabetic patient between sustainability and effectiveness of new treatments

Objectives

The increased issuance and distribution of new agents for type 2 diabetes mellitus, due to relaxed prescribing rules, has resulted not only in a greater proximity of treatments to the patient, as envisioned by post-Covidio 19 European policies, but also in an unexpected increase in healthcare spending.

Methods

An analysis of a database called "Health Card" was performed in order to evaluate all prescriptions for the new classes of medications used for type 2 diabetes.

Results

New legislation called "note 100" was introduced in early 2022, outlining the eligibility of certain categories of drugs used for the treatment of type 2 diabetes mellitus for direct prescription by primary care physicians in Italy. This investigation therefore delves into an examination of the prescribing patterns related to these drugs, contrasting the year 2021, prior to the implementation of Note 100, with the year 2022, following the incorporation of the new legislation. The result resulted in an exponential increase in prescriptions and consumption (+ 38%) and increased healthcare spending of more than three million euros for these drug categories.

Conclusion

This analysis highlights how regulation on the one hand leads to facilitating prescribing to meet a population need that is not fully satisfied, but on the other hand leads to increased prescribing and increased health care expenditures that may likely mask phenomena of prescribing inappropriateness.

Descriptive Analysis of Long- and Intermediate-Acting Insulin and Key Safety Outcomes in Adults with Type 2 Diabetes Mellitus

BACKGROUND

As new biosimilar and follow-on insulins enter the market, more data are needed on safety, effectiveness, and patterns of use for these products to inform prescriber and patient decision-making regarding treatment. Additionally, data are needed regarding real-world patterns of use to inform future studies comparing the safety and effectiveness of bio-similars to already approved agents for diabetes treatment.

OBJECTIVE

To analyze the medication use patterns, adverse events, and availability of glycated hemoglobin (A1c) values for adult patients with type 2 diabetes mellitus (T2DM) who use long-acting insulin (LAI) or neutral protamine Hagedorn (NPH), an intermediate-acting insulin.

METHODS

We used the Biologics and Biosimilars Collective Intelligence Consortium's (BBCIC) distributed research network (DRN) for this descriptive analysis. The analysis time frame was January 1, 2011, to September 30, 2015, and included patients continuously insured for at least 183 days before the first date of a filled prescription for LAI or NPH insulin alone or with rapid- or short-acting insulin or sulfonylureas, whether newly starting insulin or switching to a different product. Insulin exposure episodes were the unit of analysis, and patients were classified in cohorts according to treatment. We followed patients until end of health plan enrollment or the end of the study period. We used occurrence of a study outcome, switch to another medication regimen, discontinuation of the current medication, or study end date to mark the end of an insulin episode. We describe demographics and availability of A1c values for analysis. Study outcomes included severe hypoglycemic events and major adverse cardiac events (MACE).

RESULTS

We identified 103,951 patients with T2DM from a database of 39.1 million patients with commercial or Medicare Advantage pharmacy and medical benefits, who contributed 279,533 unique insulin exposure episodes. Most episodes (89%) included patients using LAI, and 52% of patients contributed data to 2 or more exposure cohorts. Insulin episodes lasted an average of 3.5 months, and patients had an average follow-up of 8.6 months. The unadjusted rate of severe hypoglycemic events requiring medical attention was 96.9 per 10,000 patient-years at risk (10kPYR). The unadjusted incident MACE rate was 676.9 events per 10kPYR. 38,330 T2DM patients in the BBCIC DRN had a baseline A1c available, and of those, less than 50% had a follow-up A1c result.

CONCLUSIONS

Among patients with T2DM, our observed insulin patterns of use and rates of severe hypoglycemic outcomes and MACE are consistent with other studies. We noted a paucity of A1c results available, which implies that additional data sources may be needed to augment the BBCIC DRN.

DISCLOSURES

This study was coordinated and funded by the Biologics and Biosimilars Collective Intelligence Consortium (BBCIC) and represents the independent findings of the BBCIC Insulins Principal Investigator and the BBCIC Insulins Research Team. Lockhart is employed by the BBCIC and the Academy of Managed Care Pharmacy (AMCP). Eichelberger was employed by the BBCIC and AMCP at the time of this study. McMahill-Walraven is employed by Aetna, a CVS Health business. Panozzo, Marshall, and Brown are employed by Harvard Pilgrim Healthcare Institute. Aetna was reimbursed for data and analytic support from Harvard Pilgrim Healthcare Institute and the Reagan Udall Foundation for the U.S. Food and Drug Administration. Aetna receives external funding through research grants and subcontracts with Harvard Pilgrim Healthcare Institute, which are funded by the FDA, NIH, PCORI, BBCIC, Pfizer, and GSK; the Reagan-Udall Foundation for IMEDS; and PCORI for the ADAPTABLE Study. This work was previously presented as a poster at AMCP Nexus 2018; October 22-25, 2018; in Orlando, FL.

Harnessing the Biologics and Biosimilars Collective Intelligence Consortium to Evaluate Patterns of Care

INTRODUCTION

As clinical trials test efficacy rather than effectiveness of medications, real-world effectiveness data often vary from clinical trial data. Given the recent market entry of multiple biologics and biosimilars, a dedicated assessment of these diverse agents is needed to build the evidence base regarding efficacy and safety of innovator biologics and biosimilars.

PROGRAM DESCRIPTION

The Academy of Managed Care Pharmacy's Biologics and Biosimilars Collective Intelligence Consortium (BBCIC) was convened to address the lack of real-world, postmarket outcome evidence generation for innovator biologics and corresponding biosimilars. The BBCIC is a multistakeholder scientific research consortium whose participants prioritize topics and collaboratively conduct research studies. The BBCIC conducts a wide range of analyses, including population characterization, epidemiologic studies, and active observational studies, and develops best practices for conducting large-scale studies to provide real-world evidence.

OBSERVATIONS

Over the past 3 years, we undertook multiple descriptive analyses with the goal of characterizing data availability and demonstrating the feasibility and efficacy of using the BBCIC distributed research network (DRN), which includes commercial claims data from 2008-2018 covering approximately 100 million lives, with approximately 20 million active members in 2017 from 2 major U.S. health plans and 3 regional integrated delivery networks. We analyzed 4 medication classes of particular interest to biologics and biosimilars development: insulins, granulocyte colony-stimulating factors, erythropoietic-stimulating agents, and anti-inflammatories. We were able to identify exposures and user characteristics in all 4 categories. Herein we describe the successes and challenges of conducting some of our analyses, specifically among insulin users with type 1 diabetes mellitus.

IMPLICATIONS

Our results demonstrate the BBCIC DRN's ability to identify and characterize exposures, cohorts, and outcomes that can contribute to more sophisticated comparative surveillance of biosimilars and innovator biologics in the future. Additional linkages to laboratory data and a wider range of insurance carriers will further strengthen the BBCIC DRN.

DISCLOSURES

This study was coordinated and funded by the Biologics and Biosimilars Collective Intelligence Consortium (BBCIC) and represents the independent findings of the BBCIC Insulins Principal Investigator and the BBCIC Insulins Research Team. Lockhart is employed by the BBCIC; Eichelberger was employed by the BBCIC at the time of this study. McMahill-Walraven is employed by Aetna, a CVS Health business. Panozzo, Marshall, and Brown are employed by Harvard Pilgrim Healthcare Institute. Aetna receives external funding through research grants and subcontracts with Harvard Pilgrim Healthcare Institute, which are funded by the FDA, NIH, PCORI, BBCIC, Pfizer, and GSK; the Reagan-Udall Foundation for IMEDS; and PCORI for the ADAPTABLE Study. Aetna was reimbursed for data and analytic support from Harvard Pilgrim Healthcare Institute and the Reagan Udall Foundation for the U.S. Food and Drug Administration. This work was presented as a poster at AMCP Nexus 2018; October 22-25, 2018; in Orlando, FL.

Leveraging the entire cohort in drug safety monitoring: part 1 methods for sequential surveillance that use regression adjustment or weighting to control confounding in a multisite, rare event, distributed data setting

OBJECTIVE

Study designs involving self-controlled or exposure-matched samples are commonly used to monitor postmarket vaccine and drug safety, and they use a subset of the available larger cohort. This article overviews group sequential methods designed for observational data safety monitoring that use the whole exposed and unexposed cohorts by implementing regression adjustment or weighting to control confounding.

METHODS

We summarize what is known about the performance of "whole cohort" methods in multisite health plan data networks such as the Sentinel System of the Food and Drug Administration, where outcomes are rare, individual-level patient data cannot be pooled across sites, site heterogeneity is large, and data are dynamically updated over time.

RESULTS

Group sequential estimation and testing methods that use regression or weighting can flexibly handle electronic health care data's unpredictability, including an uncertain rate of new product uptake, variable composition of the population over time, and data changes due to dynamic administrative updates. Regression and weighting methods generally have higher power, faster signal detection, and fewer practical challenges compared with some design-based confounder adjustment methods.

CONCLUSION

Group sequential regression adjustment and weighting approaches are feasible and underused in practice. They leverage more information than designs that involved sampling and increase power to detect rare adverse effects without increasing bias.

Applying sequential surveillance methods that use regression adjustment or weighting to control confounding in a multisite, rare-event, distributed setting: Part 2 in-depth example of a reanalysis of the measles-mumps-rubella-varicella combination vaccine and seizure risk

OBJECTIVE

In-depth example of two new group sequential methods for postmarket safety monitoring of new medical products.

STUDY DESIGN AND SETTING

Existing trial-based group sequential approaches have been extended to adjust for confounders, accommodate rare events, and address privacy-related constraints on data sharing. Most adaptations have involved design-based confounder strategies, for example, self-controlled or exposure matching, while analysis-based approaches like regression and weighting have received less attention. We describe the methodology of two new group sequential approaches that use analysis-based confounder adjustment (GS GEE) and weighting (GS IPTW). Using data from the Food and Drug Administration's Sentinel network, we apply both methods in the context of a known positive association: the measles-mumps-rubella-varicella vaccine and seizure risk in infants.

RESULTS

Estimates from both new approaches were similar and comparable to prior studies using design-based methods to address confounding. The time to detection of a safety signal was considerably shorter for GS IPTW, which estimates a risk difference, compared to GS GEE, which provides relative estimates of excess risk.

CONCLUSION

Future group sequential safety surveillance efforts should consider analysis-based confounder adjustment techniques that evaluate safety signals on the risk difference scale to achieve greater statistical power and more timely results.

Validity of Privacy-Protecting Analytical Methods That Use Only Aggregate-Level Information to Conduct Multivariable-Adjusted Analysis in Distributed Data Networks

Distributed data networks enable large-scale epidemiologic studies, but protecting privacy while adequately adjusting for a large number of covariates continues to pose methodological challenges. Using 2 empirical examples within a 3-site distributed data network, we tested combinations of 3 aggregate-level data-sharing approaches (risk-set, summary-table, and effect-estimate), 4 confounding adjustment methods (matching, stratification, inverse probability weighting, and matching weighting), and 2 summary scores (propensity score and disease risk score) for binary and time-to-event outcomes. We assessed the performance of combinations of these data-sharing and adjustment methods by comparing their results with results from the corresponding pooled individual-level data analysis (reference analysis). For both types of outcomes, the method combinations examined yielded results identical or comparable to the reference results in most scenarios. Within each data-sharing approach, comparability between aggregate- and individual-level data analysis depended on adjustment method; for example, risk-set data-sharing with matched or stratified analysis of summary scores produced identical results, while weighted analysis showed some discrepancies. Across the adjustment methods examined, risk-set data-sharing generally performed better, while summary-table and effect-estimate data-sharing more often produced discrepancies in settings with rare outcomes and small sample sizes. Valid multivariable-adjusted analysis can be performed in distributed data networks without sharing of individual-level data.

Cardiovascular outcomes of sodium glucose cotransporter-2 inhibitors in patients with type 2 diabetes

AIMS

To determine the association between cardiovascular diseases (CVD) and SGLT2 inhibitors compared to sulfonylureas and dipeptidyl peptidase-4 (DPP4) inhibitors and to examine within-class effects of SGLT2 inhibitors.

METHODS

A retrospective cohort analysis was conducted using Truven Health MarketScan. New users of SGLT2 inhibitors, sulfonylureas or DPP-4 inhibitors were included. Primary outcome was incident CVD, defined as non-fatal myocardial infarction or non-fatal stroke; secondary outcomes were hospitalization because of heart failure and lower extremity amputation. Proportional hazards models, after propensity score matching, were used to obtain hazard ratios (HR) and 95% confidence intervals (CI).

RESULTS

In fully adjusted models, use of SGLT2 inhibitors was associated with a decreased risk of developing CVD compared with use of sulfonylureas (HR, 0.50; 95% CI, 0.45, 0.55) and DPP-4 inhibitors (HR, 0.57; 95% CI, 0.52, 0.62), respectively. Analyses revealed no evidence of within-class effects: dapagliflozin vs sulfonylureas (HR, 0.55; 95% CI, 0.43, 0.70) or DPP-4 inhibitors (HR, 0.57; 95% CI, 0.46, 0.70); and canagliflozin vs sulfonylureas (HR, 0.61; 95% CI, 0.54, 0.69) or DPP-4 inhibitors (HR, 0.66; 95% CI, 0.54, 0.71). Additionally, SGLT2 inhibitors were associated with lower risk of hospitalization because of heart failure compared to both sulfonylureas and DPP-4 inhibitors, as well as lower risk of lower extremity amputation compared to sulfonylureas.

CONCLUSION

Using population-based data, incident use of SGLT-2 inhibitors was associated with a decreased incidence of CVD compared to use of sulfonylureas and DPP-4 inhibitors. These findings were consistent between dapagliflozin and canagliflozin, suggesting that CVD reduction is a class effect for SGLT2 inhibitors. In addition, SGLT2 inhibitors portended lower risk of hospitalization because of heart failure (vs sulfonylureas and DPP-4 inhibitors) and lower risk of lower extremity amputation (vs sulfonylureas).

Testosterone Therapy and Risk of Acute Myocardial Infarction in Hypogonadal Men: An Administrative Health Care Claims Study

BACKGROUND

There are some ongoing debates on the potential link between testosterone therapy (TT) and risk of acute myocardial infarction (MI).

AIM

To investigate the association between acute MI and TT use compared with non-use in men having documented hypogonadism (diagnostic International Classification of Diseases, Ninth Revision codes 257.2, 257.8, 257.9, 758.7) in patient claims records.

METHODS

This retrospective cohort study used a real-world US-based administrative health care claims database (MarketScan 2004-2013; Truven Health Analytics, Ann Arbor, MI, USA) to compare MI rates between TT-treated men and a cohort of untreated hypogonadal men matched by a calendar time-specific propensity score. Subgroup analyses were performed by route of administration, age, and prior cardiovascular disease (CVD).

OUTCOMES

Incidence rates of MI (per 1,000 person-years) and hazard ratio.

RESULTS

After 1:1 calendar time-specific propensity score matching, 207,176 TT-treated men and 207,176 untreated hypogonadal men were included in the analysis (mean age = 51.8 years). Incidence rates of MI were 4.20 (95% CI = 3.87-4.52) in the TT-treated cohort and 4.67 (95% CI = 4.43-4.90) in the untreated hypogonadal cohort. Cox regression model showed no significant association between TT use and MI when comparing TT-treated with untreated hypogonadal men overall (hazard ratio = 0.99, 95% CI = 0.89-1.09), by age, or by prior CVD. A significant association was observed when comparing a subgroup of injectable (short- and long-acting combined) TT users with untreated hypogonadal men (hazard ratio = 1.55, 95% CI = 1.24-1.93).

CLINICAL IMPLICATION

In this study, there was no association between TT (overall) and risk of acute MI.

STRENGTHS AND LIMITATIONS

Strengths included the use of a comprehensive real-world database, sophisticated matching based on calendar blocks of 6 months to decrease potential bias in this observational study, carefully chosen index dates for the untreated cohort to avoid immortal time bias, and implemented sensitivity analysis to further investigate the findings (stratification by administration route, age, and prior CVD). Key limitations included no information about adherence, hypogonadism condition based solely on diagnosis (no information on clinical symptoms or testosterone levels), lack of information on disease severity, inability to capture diagnoses, medical procedures, and medicine dispensing if corresponding billing codes were not generated and findings could contain biases or fail to generalize well to other populations.

CONCLUSION

This large, retrospective, real-world observational study showed no significant association between TT use and acute MI when comparing TT-treated with untreated hypogonadal men overall, by age, or by prior CVD; the suggested association between injectable TT and acute MI deserves further investigation. Li H, Mitchell L, Zhang X, et al. Testosterone Therapy and Risk of Acute Myocardial Infarction in Hypogonadal Men: An Administrative Health Care Claims Study. J Sex Med 2017;14:1307-1317.

Risk of heart failure hospitalization among users of dipeptidyl peptidase-4 inhibitors compared to glucagon-like peptide-1 receptor agonists

Background

Incretin-based therapies including dipeptidyl peptidase-4 (DPP-4) inhibitors and glucagon like peptide-1 (GLP-1) receptor agonists are novel medications for type 2 diabetes management. Several studies have found cardioprotective effects of incretin-based therapies; however, it remains unclear whether there is any difference in heart failure (HF) risk between the two incretin-based therapies (DPP-4 inhibitors and GLP-1 receptor agonists). We aimed to assess the risk of hospitalization due to HF with the use of DPP-4 inhibitors compared to GLP-1 receptor agonists.

Methods

Using Truven Health Marketscan data, we conducted a retrospective cohort study of patients with type 2 diabetes, who were newly initiated on DPP-4 inhibitors or GLP-1 agonists. Follow-up continued from drug initiation until the first occurrence of: HF hospitalization (primary outcome), discontinuation of therapy (i.e. no fill for 7 days), switch to the comparator, end of enrollment, or end of study (December 2013). Cox proportional hazards models with propensity-score-matching were used to compare the risk of HF hospitalization between DPP-4 inhibitors and GLP-1 agonists.

Results

A total of 321,606 propensity score-matched patients were included in the analysis (n = 160,803 for DPP-4 inhibitors; n = 160,803 for GLP-1 agonists). After adjusting for baseline characteristics and disease risk factors, the use of DPP-4 inhibitors was associated with a 14% decreased risk of HF hospitalization compared to GLP-1 agonists use [hazard ratio (HR), 0.86; 95% confidence interval (CI) 0.83, 0.90]. The results were consistent in patients without baseline HF (HR, 0.85; 95% CI 0.82, 0.89), but the association was not statistically significant for patients with baseline HF (HR, 0.90; 95% CI 0.74, 1.07).

Conclusion

In this retrospective matched cohort of patients with type 2 diabetes, the use of DPP-4 inhibitors was associated with a reduced risk of HF hospitalization compared to GLP-1 agonists. However, the association was not statistically significant in patients who had HF prior to the use of DPP-4 inhibitors.

Electronic supplementary material

The online version of this article (10.1186/s12933-018-0746-4) contains supplementary material, which is available to authorized users.

Near Real-time Surveillance for Consequences of Health Policies Using Sequential Analysis

BACKGROUND

New health policies may have intended and unintended consequences. Active surveillance of population-level data may provide initial signals of policy effects for further rigorous evaluation soon after policy implementation.

OBJECTIVE

This study evaluated the utility of sequential analysis for prospectively assessing signals of health policy impacts. As a policy example, we studied the consequences of the widely publicized Food and Drug Administration's warnings cautioning that antidepressant use could increase suicidal risk in youth.

METHOD

This was a retrospective, longitudinal study, modeling prospective surveillance, using the maximized sequential probability ratio test. We used historical data (2000-2010) from 11 health systems in the US Mental Health Research Network. The study cohort included adolescents (ages 10-17 y) and young adults (ages 18-29 y), who were targeted by the warnings, and adults (ages 30-64 y) as a comparison group. Outcome measures were observed and expected events of 2 possible unintended policy outcomes: psychotropic drug poisonings (as a proxy for suicide attempts) and completed suicides.

RESULTS

We detected statistically significant (P<0.05) signals of excess risk for suicidal behavior in adolescents and young adults within 5-7 quarters of the warnings. The excess risk in psychotropic drug poisonings was consistent with results from a previous, more rigorous interrupted time series analysis but use of the maximized sequential probability ratio test method allows timely detection. While we also detected signals of increased risk of completed suicide in these younger age groups, on its own it should not be taken as conclusive evidence that the policy caused the signal. A statistical signal indicates the need for further scrutiny using rigorous quasi-experimental studies to investigate the possibility of a cause-and-effect relationship.

CONCLUSIONS

This was a proof-of-concept study. Prospective, periodic evaluation of administrative health care data using sequential analysis can provide timely population-based signals of effects of health policies. This method may be useful to use as new policies are introduced.

Prospective Postmarketing Surveillance of Acute Myocardial Infarction in New Users of Saxagliptin: A Population-Based Study

OBJECTIVE

The cardiovascular safety of saxagliptin, a dipeptidyl-peptidase 4 inhibitor, compared with other antihyperglycemic treatments is not well understood. We prospectively examined the association between saxagliptin use and acute myocardial infarction (AMI).

RESEARCH DESIGN AND METHODS

We identified patients aged ≥18 years, starting from the approval date of saxagliptin in 2009 and continuing through August 2014, using data from 18 Mini-Sentinel data partners. We conducted seven sequential assessments comparing saxagliptin separately with sitagliptin, pioglitazone, second-generation sulfonylureas, and long-acting insulin, using disease risk score (DRS) stratification and propensity score (PS) matching to adjust for potential confounders. Sequential testing kept the overall chance of a false-positive signal below 0.05 (one-sided) for each pairwise comparison.

RESULTS

We identified 82,264 saxagliptin users and more than 1.5 times as many users of each comparator. At the end of surveillance, the DRS-stratified hazard ratios (HRs) (95% CI) were 1.08 (0.90-1.28) in the comparison with sitagliptin, 1.11 (0.87-1.42) with pioglitazone, 0.79 (0.64-0.98) with sulfonylureas, and 0.57 (0.46-0.70) with long-acting insulin. The corresponding PS-matched HRs were similar. Only one interim analysis of 168 analyses met criteria for a safety signal: the PS-matched saxagliptin-pioglitazone comparison from the fifth sequential analysis, which yielded an HR of 1.63 (1.12-2.37). This association diminished in subsequent analyses.

CONCLUSIONS

We did not find a higher AMI risk in saxagliptin users compared with users of other selected antihyperglycemic agents during the first 5 years after U.S. Food and Drug Administration approval of the drug.

Assessing performance of sequential analysis methods for active drug safety surveillance using observational data

The routine use of sequential methods is well established in clinical studies. Recently, there has been increasing interest in applying these methods to prospectively monitor the safety of newly approved drugs through accrual of real-world data. However, the application to marketed drugs using real-world data has been limited and work is needed to determine which sequential approaches are most suited to such data. In this study, the conditional sequential sampling procedure (CSSP), a group sequential method, was compared with a log-linear model with Poisson distribution (LLMP) through a SAS procedure (PROC GENMOD) combined with an alpha-spending function on two large longitudinal US administrative health claims databases. Relative performance in identifying known drug-outcome associations was examined using a set of 50 well-studied drug-outcome pairs. The study finds that neither method correctly identified all pairs but that LLMP often provides better ability and shorter time for identifying the known drug-outcome associations with superior computational performance when compared with CSSP, albeit with more false positives. With the features of flexible confounding control and ease of implementation, LLMP may be a good alternative or complement to CSSP.

Using Multiple Pharmacovigilance Models Improves the Timeliness of Signal Detection in Simulated Prospective Surveillance

INTRODUCTION

Prospective pharmacovigilance aims to rapidly detect safety concerns related to medical products. The exposure model selected for pharmacovigilance impacts the timeliness of signal detection. However, in most real-life pharmacovigilance studies, little is known about which model correctly represents the association and there is no evidence to guide the selection of an exposure model. Different exposure models reflect different aspects of exposure history, and their relevance varies across studies. Therefore, one potential solution is to apply several alternative exposure models simultaneously, with each model assuming a different exposure-risk association, and then combine the model results.

METHODS

We simulated alternative clinically plausible associations between time-varying drug exposure and the hazard of an adverse event. Prospective surveillance was conducted on the simulated data by estimating parametric and semi-parametric exposure-risk models at multiple times during follow-up. For each model separately, and using combined evidence from different subsets of models, we compared the time to signal detection.

RESULTS

Timely detection across the simulated associations was obtained by fitting a set of pharmacovigilance models. This set included alternative parametric models that assumed different exposure-risk associations and flexible models that made no assumptions regarding the form/shape of the association. Times to detection generated using a simple combination of evidence from multiple models were comparable to those observed under the ideal, but unrealistic, scenario where pharmacovigilance relied on the single 'true' model used for data generation.

CONCLUSIONS

Simulation results indicate that, if the true model is not known, an association can be detected in a more timely manner by first fitting a carefully selected set of exposure-risk models and then generating a signal as soon as any of the models considered yields a test statistic value below a predetermined testing threshold.

A Synthesis of Current Surveillance Planning Methods for the Sequential Monitoring of Drug and Vaccine Adverse Effects Using Electronic Health Care Data

INTRODUCTION

The large-scale assembly of electronic health care data combined with the use of sequential monitoring has made proactive postmarket drug- and vaccine-safety surveillance possible. Although sequential designs have been used extensively in randomized trials, less attention has been given to methods for applying them in observational electronic health care database settings.

EXISTING METHODS

We review current sequential-surveillance planning methods from randomized trials, and the Vaccine Safety Datalink (VSD) and Mini-Sentinel Pilot projects-two national observational electronic health care database safety monitoring programs.

FUTURE SURVEILLANCE PLANNING

Based on this examination, we suggest three steps for future surveillance planning in health care databases: (1) prespecify the sequential design and analysis plan, using available feasibility data to reduce assumptions and minimize later changes to initial plans; (2) assess existing drug or vaccine uptake, to determine if there is adequate information to proceed with surveillance, before conducting more resource-intensive planning; and (3) statistically evaluate and clearly communicate the sequential design with all those designing and interpreting the safety-surveillance results prior to implementation. Plans should also be flexible enough to accommodate dynamic and often unpredictable changes to the database information made by the health plans for administrative purposes.

CONCLUSIONS

This paper is intended to encourage dialogue about establishing a more systematic, scalable, and transparent sequential design-planning process for medical-product safety-surveillance systems utilizing observational electronic health care databases. Creating such a framework could yield improvements over existing practices, such as designs with increased power to assess serious adverse events.

Risk for Hospitalized Heart Failure Among New Users of Saxagliptin, Sitagliptin, and Other Antihyperglycemic Drugs: A Retrospective Cohort Study

BACKGROUND

Recent postmarketing trials produced conflicting results about the risk for hospitalized heart failure (hHF) associated with dipeptidyl peptidase-4 (DPP-4) inhibitors, creating uncertainty about the safety of these antihyperglycemic agents.

OBJECTIVE

To examine the associations of hHF with saxagliptin and sitagliptin.

DESIGN

Population-based, retrospective, new-user cohort study.

SETTING

18 health insurance and health system data partners in the U.S. Food and Drug Administration's Mini-Sentinel program.

PATIENTS

Patients aged 18 years or older with type 2 diabetes who initiated therapy with saxagliptin, sitagliptin, pioglitazone, second-generation sulfonylureas, or long-acting insulin products from 2006 to 2013.

MEASUREMENTS

Hospitalized HF, identified by International Classification of Diseases, Ninth Revision, Clinical Modification codes 402.x1, 404.x1, 404.x3, and 428.xx recorded as the principal discharge diagnosis.

RESULTS

78 553 saxagliptin users and 298 124 sitagliptin users contributed an average of 7 to 9 months of follow-up data to 1 or more pairwise comparisons. The risk for hHF was not higher with DPP-4 inhibitors than with the other study drugs. The hazard ratios from the disease risk score (DRS)-stratified analyses were 0.83 (95% CI, 0.70 to 0.99) for saxagliptin versus sitagliptin, 0.63 (CI, 0.47 to 0.85) for saxagliptin versus pioglitazone, 0.69 (CI, 0.54 to 0.87) for saxagliptin versus sulfonylureas, and 0.61 (CI, 0.50 to 0.73) for saxagliptin versus insulin. The DRS-stratified hazard ratios were 0.74 (CI, 0.64 to 0.85) for sitagliptin versus pioglitazone, 0.86 (CI, 0.77 to 0.95) for sitagliptin versus sulfonylureas, and 0.71 (CI, 0.64 to 0.78) for sitagliptin versus insulin. Results from the 1:1 propensity score-matched analyses were similar. Results were also similar in subgroups of patients with and without prior cardiovascular disease and in a subgroup defined by the 2 highest DRS deciles.

LIMITATION

Residual confounding and short follow-up.

CONCLUSION

In this large cohort study, a higher risk for hHF was not observed in users of saxagliptin or sitagliptin compared with other selected antihyperglycemic agents.

PRIMARY FUNDING SOURCE

U.S. Food and Drug Administration.

Network meta-analysis incorporating randomized controlled trials and non-randomized comparative cohort studies for assessing the safety and effectiveness of medical treatments: challenges and opportunities

Network meta-analysis is increasingly used to allow comparison of multiple treatment alternatives simultaneously, some of which may not have been compared directly in primary research studies. The majority of network meta-analyses published to date have incorporated data from randomized controlled trials (RCTs) only; however, inclusion of non-randomized studies may sometimes be considered. Non-randomized studies can complement RCTs or address some of their limitations, such as short follow-up time, small sample size, highly selected population, high cost, and ethical restrictions. In this paper, we discuss the challenges and opportunities of incorporating both RCTs and non-randomized comparative cohort studies into network meta-analysis for assessing the safety and effectiveness of medical treatments. Non-randomized studies with inadequate control of biases such as confounding may threaten the validity of the entire network meta-analysis. Therefore, identification and inclusion of non-randomized studies must balance their strengths with their limitations. Inclusion of both RCTs and non-randomized studies in network meta-analysis will likely increase in the future due to the growing need to assess multiple treatments simultaneously, the availability of higher quality non-randomized data and more valid methods, and the increased use of progressive licensing and product listing agreements requiring collection of data over the life cycle of medical products. Inappropriate inclusion of non-randomized studies could perpetuate the biases that are unknown, unmeasured, or uncontrolled. However, thoughtful integration of randomized and non-randomized studies may offer opportunities to provide more timely, comprehensive, and generalizable evidence about the comparative safety and effectiveness of medical treatments.

Sensitivity analysis of methods for active surveillance of acute myocardial infarction using electronic databases

BACKGROUND

The validity of conclusions from observational studies depends on decisions regarding design, analysis, data quality, and implementation. Through sensitivity analyses, we explored the impact of such decisions on balance control and risk estimates.

METHODS

Using as a template the Mini-Sentinel protocol for the active surveillance of acute myocardial infarction (MI) in association with use of antidiabetic agents, we defined cohorts of new users of metformin and second-generation sulfonylureas, baseline covariates and acute MI events using three combinations of washout and baseline periods. Using propensity-score matching, we assessed balance control and risk estimates using cumulative data for matching all patients compared with not rematching prior matches in quarterly analyses over the follow-up period.

RESULTS

A longer washout period increased the confidence in new-user status, but at the expense of sample size; a longer baseline period improved capture of covariates related to pre-existing chronic conditions. When all patients were matched each quarter, balance was improved and risk estimates were more robust, especially in the later quarters.

CONCLUSIONS

Durations of washout and baseline periods influence the likelihood of new-user status and sample size. Matching all patients tends to result in better covariate balance than matching only new patients. Decisions regarding the durations of washout and baseline periods depend on the specific research question and availability of longitudinal patient data within the database. This paper demonstrates the importance and utility of sensitivity analysis of methods for evaluating the robustness of results in observational studies.

Empirical performance of a new user cohort method: lessons for developing a risk identification and analysis system

BACKGROUND

Observational healthcare data offer the potential to enable identification of risks of medical products, but appropriate methodology has not yet been defined. The new user cohort method, which compares the post-exposure rate among the target drug to a referent comparator group, is the prevailing approach for many pharmacoepidemiology evaluations and has been proposed as a promising approach for risk identification but its performance in this context has not been fully assessed.

OBJECTIVES

To evaluate the performance of the new user cohort method as a tool for risk identification in observational healthcare data.

RESEARCH DESIGN

The method was applied to 399 drug-outcome scenarios (165 positive controls and 234 negative controls across 4 health outcomes of interest) in 5 real observational databases (4 administrative claims and 1 electronic health record) and in 6 simulated datasets with no effect and injected relative risks of 1.25, 1.5, 2, 4, and 10, respectively.

MEASURES

Method performance was evaluated through Area Under ROC Curve (AUC), bias, and coverage probability.

RESULTS

The new user cohort method achieved modest predictive accuracy across the outcomes and databases under study, with the top-performing analysis near AUC >0.70 in most scenarios. The performance of the method was particularly sensitive to the choice of comparator population. For almost all drug-outcome pairs there was a large difference, either positive or negative, between the true effect size and the estimate produced by the method, although this error was near zero on average. Simulation studies showed that in the majority of cases, the true effect estimate was not within the 95 % confidence interval produced by the method.

CONCLUSION

The new user cohort method can contribute useful information toward a risk identification system, but should not be considered definitive evidence given the degree of error observed within the effect estimates. Careful consideration of the comparator selection and appropriate calibration of the effect estimates is required in order to properly interpret study findings.

Assessing the impact of propensity score estimation and implementation on covariate balance and confounding control within and across important subgroups in comparative effectiveness research

PURPOSE

Researchers are often interested in estimating treatment effects in subgroups controlling for confounding based on a propensity score (PS) estimated in the overall study population.

OBJECTIVE

To evaluate covariate balance and confounding control in sulfonylurea versus metformin initiators within subgroups defined by cardiovascular disease (CVD) history comparing an overall PS with subgroup-specific PSs implemented by 1:1 matching and stratification.

METHODS

We analyzed younger patients from a US insurance claims database and older patients from 2 Medicare (Humana Medicare Advantage, fee-for-service Medicare Parts A, B, and D) datasets. Confounders and risk factors for acute myocardial infarction were included in an overall PS and subgroup PSs with and without CVD. Covariate balance was assessed using the average standardized absolute mean difference (ASAMD).

RESULTS

Compared with crude estimates, ASAMD across covariates was improved 70%-94% for stratification for Medicare cohorts and 44%-99% for the younger cohort, with minimal differences between overall and subgroup-specific PSs. With matching, 75%-99% balance improvement was achieved regardless of cohort and PS, but with smaller sample size. Hazard ratios within each CVD subgroup differed minimally among PS and cohorts.

CONCLUSIONS

Both overall PSs and CVD subgroup-specific PSs achieved good balance on measured covariates when assessing the relative association of diabetes monotherapy with nonfatal myocardial infarction. PS matching generally led to better balance than stratification, but with smaller sample size. Our study is limited insofar as crude differences were minimal, suggesting that the new user, active comparator design identified patients with some equipoise between treatments.

Medical product safety surveillance: how many databases to use?

Large linked database networks, like the US Food and Drug Administration's Sentinel System, are being built for medical product surveillance. One use of these networks is for "near real-time" sequential database surveillance of prespecified medical product-adverse event pairs, which may result in a "safety signal" when a statistical excess risk is detected. Sequential database surveillance requires the investigator to manage surveillance in both information time (ie, how sample size accrues) and calendar time. Calendar time is important because people external to the surveillance population may be affected by the speed with which a safety signal is detected or ruled out. Optimal design and analysis aspects of sequential database surveillance are not well developed, but are gaining in importance as observational database networks grow. Using information time concepts, we show how to calculate sample sizes when performing sequential database surveillance, illustrating the relationships between statistical power, the time to detect a signal, and the maximum sample size for various true effect sizes. Then, using a vaccine example, we demonstrate a four-step planning process that allows investigators to translate information time into calendar time. Given the calendar time for surveillance, the process focuses on choosing observational database configurations consistent with the investigator's preferences for timeliness and statistical power. Although the planning process emphasizes sample size considerations, the influence of secondary database attributes such as delay times, measurement error, and cost are also discussed. Appropriate planning allows the most efficient use of public health dollars dedicated to medical product surveillance efforts.

The U.S. Food and Drug Administration's Mini-Sentinel program: status and direction

The Mini-Sentinel is a pilot program that is developing methods, tools, resources, policies, and procedures to facilitate the use of routinely collected electronic healthcare data to perform active surveillance of the safety of marketed medical products, including drugs, biologics, and medical devices. The U.S. Food and Drug Administration (FDA) initiated the program in 2009 as part of its Sentinel Initiative, in response to a Congressional mandate in the FDA Amendments Act of 2007. After two years, Mini-Sentinel includes 31 academic and private organizations. It has developed policies, procedures, and technical specifications for developing and operating a secure distributed data system comprised of separate data sets that conform to a common data model covering enrollment, demographics, encounters, diagnoses, procedures, and ambulatory dispensing of prescription drugs. The distributed data sets currently include administrative and claims data from 2000 to 2011 for over 300 million person-years, 2.4 billion encounters, 38 million inpatient hospitalizations, and 2.9 billion dispensings. Selected laboratory results and vital signs data recorded after 2005 are also available. There is an active data quality assessment and characterization program, and eligibility for medical care and pharmacy benefits is known. Systematic reviews of the literature have assessed the ability of administrative data to identify health outcomes of interest, and procedures have been developed and tested to obtain, abstract, and adjudicate full-text medical records to validate coded diagnoses. Mini-Sentinel has also created a taxonomy of study designs and analytical approaches for many commonly occurring situations, and it is developing new statistical and epidemiologic methods to address certain gaps in analytic capabilities. Assessments are performed by distributing computer programs that are executed locally by each data partner. The system is in active use by FDA, with the majority of assessments performed using customizable, reusable queries (programs). Prospective and retrospective assessments that use customized protocols are conducted as well. To date, several hundred unique programs have been distributed and executed. Current activities include active surveillance of several drugs and vaccines, expansion of the population, enhancement of the common data model to include additional types of data from electronic health records and registries, development of new methodologic capabilities, and assessment of methods to identify and validate additional health outcomes of interest.

Design for validation of acute myocardial infarction cases in Mini-Sentinel

PURPOSE

To describe the acute myocardial infarction (AMI) validation project, a test case for health outcome validation within the US Food and Drug Administration-funded Mini-Sentinel pilot program.

METHODS

The project consisted of four parts: (i) case identification-developing an algorithm based on the International Classification of Diseases, Ninth Revision, to identify hospitalized AMI patients within the Mini-Sentinel Distributed Database; (ii) chart retrieval-establishing procedures that ensured patient privacy (collection and transfer of minimum necessary amount of information, and redaction of direct identifiers to validate potential cases of AMI); (iii) abstraction and adjudication-trained nurse abstractors gathered key data using a standardized form with cardiologist adjudication; and (iv) calculation of the positive predictive value of the constructed algorithm.

RESULTS

Key decision points included (i) breadth of the AMI algorithm, (ii) centralized versus distributed abstraction, and (iii) approaches to maintaining patient privacy and to obtaining charts for public health purposes. We used an algorithm limited to International Classification of Diseases, Ninth Revision, codes 410.x0-410.x1. Centralized data abstraction was performed because of the modest number of charts requested (<155). The project's public health status accelerated chart retrieval in most instances.

CONCLUSIONS

We have established a process to validate AMI within Mini-Sentinel, which may be used for other health outcomes. Challenges include the following: (i) ensuring that only minimum necessary data are transmitted by Data Partners for centralized chart review, (ii) establishing procedures to maintain data privacy while still allowing for timely access to medical charts, and (iii) securing access to charts for public health uses that do not require approval from an institutional review board while maintaining patient privacy.