Signaling COVID-19 Vaccine Adverse Events.

INTRODUCTION: Statistical signal detection is a crucial tool for rapidly identifying potential risks associated with pharmaceutical products. The unprecedented environment created by the coronavirus disease 2019 (COVID-19) pandemic for vaccine surveillance predisposes commonly applied signal detection methodologies to a statistical issue called the masking effect, in which signals for a vaccine of interest are hidden by the presence of other reported vaccines. This masking effect may in turn limit or delay our understanding of the risks associated with new and established vaccines. OBJECTIVE: The aim is to investigate the problem of masking in the context of COVID-19 vaccine signal detection, assessing its impact, extent, and root causes. METHODS: Based on data underlying the Vaccine Adverse Event Reporting System, three commonly applied statistical signal detection methodologies, and a more advanced regression-based methodology, we investigate the temporal evolution of signals corresponding to five largely recognized adverse events and two potentially new adverse events. RESULTS: The results demonstrate that signals of adverse events related to COVID-19 vaccines may be undetected or delayed due to masking when generated by methodologies currently utilized by pharmacovigilance organizations, and that a class of advanced methodologies can partially alleviate the problem. The results indicate that while masking is rare relative to all possible statistical associations, it is much more likely to occur in COVID-19 vaccine signaling, and that its extent, direction, impact, and roots are not static, but rather changing in accordance with the changing nature of data. CONCLUSIONS: Masking is an addressable problem that merits careful consideration, especially in situations such as COVID-19 vaccine safety surveillance and other emergency use authorization products.

Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology.

BACKGROUND: Adverse drug reactions (ADRs) are one of the leading causes of morbidity and mortality in health care. Understanding which drug targets are linked to ADRs can lead to the development of safer medicines. METHODS: Here, we analyse in vitro secondary pharmacology of common (off) targets for 2134 marketed drugs. To associate these drugs with human ADRs, we utilized FDA Adverse Event Reports and developed random forest models that predict ADR occurrences from in vitro pharmacological profiles. FINDINGS: By evaluating Gini importance scores of model features, we identify 221 target-ADR associations, which co-occur in PubMed abstracts to a greater extent than expected by chance. Amongst these are established relations, such as the association of in vitro hERG binding with cardiac arrhythmias, which further validate our machine learning approach. Evidence on bile acid metabolism supports our identification of associations between the Bile Salt Export Pump and renal, thyroid, lipid metabolism, respiratory tract and central nervous system disorders. Unexpectedly, our model suggests PDE3 is associated with 40 ADRs. INTERPRETATION: These associations provide a comprehensive resource to support drug development and human biology studies. FUNDING: This study was not supported by any formal funding bodies.

Toward multimodal signal detection of adverse drug reactions.

OBJECTIVE: Improving mechanisms to detect adverse drug reactions (ADRs) is key to strengthening post-marketing drug safety surveillance. Signal detection is presently unimodal, relying on a single information source. Multimodal signal detection is based on jointly analyzing multiple information sources. Building on, and expanding the work done in prior studies, the aim of the article is to further research on multimodal signal detection, explore its potential benefits, and propose methods for its construction and evaluation. MATERIAL AND METHODS: Four data sources are investigated; FDA's adverse event reporting system, insurance claims, the MEDLINE citation database, and the logs of major Web search engines. Published methods are used to generate and combine signals from each data source. Two distinct reference benchmarks corresponding to well-established and recently labeled ADRs respectively are used to evaluate the performance of multimodal signal detection in terms of area under the ROC curve (AUC) and lead-time-to-detection, with the latter relative to labeling revision dates. RESULTS: Limited to our reference benchmarks, multimodal signal detection provides AUC improvements ranging from 0.04 to 0.09 based on a widely used evaluation benchmark, and a comparative added lead-time of 7-22 months relative to labeling revision dates from a time-indexed benchmark. CONCLUSIONS: The results support the notion that utilizing and jointly analyzing multiple data sources may lead to improved signal detection. Given certain data and benchmark limitations, the early stage of development, and the complexity of ADRs, it is currently not possible to make definitive statements about the ultimate utility of the concept. Continued development of multimodal signal detection requires a deeper understanding the data sources used, additional benchmarks, and further research on methods to generate and synthesize signals.

Reverse translation of adverse event reports paves the way for de-risking preclinical off-targets.

The Food and Drug Administration Adverse Event Reporting System (FAERS) remains the primary source for post-marketing pharmacovigilance. The system is largely un-curated, unstandardized, and lacks a method for linking drugs to the chemical structures of their active ingredients, increasing noise and artefactual trends. To address these problems, we mapped drugs to their ingredients and used natural language processing to classify and correlate drug events. Our analysis exposed key idiosyncrasies in FAERS, for example reports of thalidomide causing a deadly ADR when used against myeloma, a likely result of the disease itself; multiplications of the same report, unjustifiably increasing its importance; correlation of reported ADRs with public events, regulatory announcements, and with publications. Comparing the pharmacological, pharmacokinetic, and clinical ADR profiles of methylphenidate, aripiprazole, and risperidone, and of kinase drugs targeting the VEGF receptor, demonstrates how underlying molecular mechanisms can emerge from ADR co-analysis. The precautions and methods we describe may enable investigators to avoid confounding chemistry-based associations and reporting biases in FAERS, and illustrate how comparative analysis of ADRs can reveal underlying mechanisms.

Early identification of adverse drug reactions from search log data.

The timely and accurate identification of adverse drug reactions (ADRs) following drug approval is a persistent and serious public health challenge. Aggregated data drawn from anonymized logs of Web searchers has been shown to be a useful source of evidence for detecting ADRs. However, prior studies have been based on the analysis of established ADRs, the existence of which may already be known publically. Awareness of these ADRs can inject existing knowledge about the known ADRs into online content and online behavior, and thus raise questions about the ability of the behavioral log-based methods to detect new ADRs. In contrast to previous studies, we investigate the use of search logs for the early detection of known ADRs. We use a large set of recently labeled ADRs and negative controls to evaluate the ability of search logs to accurately detect ADRs in advance of their publication. We leverage the Internet Archive to estimate when evidence of an ADR first appeared in the public domain and adjust the index date in a backdated analysis. Our results demonstrate how search logs can be used to detect new ADRs, the central challenge in pharmacovigilance.

A time-indexed reference standard of adverse drug reactions.

Undetected adverse drug reactions (ADRs) pose a major burden on the health system. Data mining methodologies designed to identify signals of novel ADRs are of deep importance for drug safety surveillance. The development and evaluation of these methodologies requires proper reference benchmarks. While progress has recently been made in developing such benchmarks, our understanding of the performance characteristics of the data mining methodologies is limited because existing benchmarks do not support prospective performance evaluations. We address this shortcoming by providing a reference standard to support prospective performance evaluations. The reference standard was systematically curated from drug labeling revisions, such as new warnings, which were issued and communicated by the US Food and Drug Administration in 2013. The reference standard includes 62 positive test cases and 75 negative controls, and covers 44 drugs and 38 events. We provide usage guidance and empirical support for the reference standard by applying it to analyze two data sources commonly mined for drug safety surveillance.

Interpreting observational studies: why empirical calibration is needed to correct p-values.

Often the literature makes assertions of medical product effects on the basis of ' p < 0.05'. The underlying premise is that at this threshold, there is only a 5% probability that the observed effect would be seen by chance when in reality there is no effect. In observational studies, much more than in randomized trials, bias and confounding may undermine this premise. To test this premise, we selected three exemplar drug safety studies from literature, representing a case-control, a cohort, and a self-controlled case series design. We attempted to replicate these studies as best we could for the drugs studied in the original articles. Next, we applied the same three designs to sets of negative controls: drugs that are not believed to cause the outcome of interest. We observed how often p < 0.05 when the null hypothesis is true, and we fitted distributions to the effect estimates. Using these distributions, we compute calibrated p-values that reflect the probability of observing the effect estimate under the null hypothesis, taking both random and systematic error into account. An automated analysis of scientific literature was performed to evaluate the potential impact of such a calibration. Our experiment provides evidence that the majority of observational studies would declare statistical significance when no effect is present. Empirical calibration was found to reduce spurious results to the desired 5% level. Applying these adjustments to literature suggests that at least 54% of findings with p < 0.05 are not actually statistically significant and should be reevaluated.

Evaluation of disproportionality safety signaling applied to healthcare databases.

OBJECTIVE: To evaluate the performance of a disproportionality design, commonly used for analysis of spontaneous reports data such as the FDA Adverse Event Reporting System database, as a potential analytical method for an adverse drug reaction risk identification system using healthcare data. RESEARCH DESIGN: We tested the disproportionality design in 5 real observational healthcare databases and 6 simulated datasets, retrospectively studying the predictive accuracy of the method when applied to a collection of 165 positive controls and 234 negative controls across 4 outcomes: acute liver injury, acute myocardial infarction, acute kidney injury, and upper gastrointestinal bleeding. MEASURES: We estimate how well the method can be expected to identify true effects and discriminate from false findings and explore the statistical properties of the estimates the design generates. The primary measure was the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. RESULTS: For each combination of 4 outcomes and 5 databases, 48 versions of disproportionality analysis (DPA) were carried out and the AUC computed. The majority of the AUC values were in the range of 0.35 < AUC < 0.6, which is considered to be poor predictive accuracy, since the value AUC = 0.5 would be expected from mere random assignment. Several DPA versions achieved AUC of about 0.7 for the outcome Acute Renal Failure within the GE database. The overall highest DPA version across all 20 outcome-database combinations was the Bayesian Information Component method with no stratification by age and gender, using first occurrence of outcome and with assumed time-at-risk equal to duration of exposure + 30 d, but none were uniformly optimal. The relative risk estimates for the negative control drug-event combinations were very often biased either upward or downward by a factor of 2 or more. Coverage probabilities of confidence intervals from all methods were far below nominal. CONCLUSIONS: The disproportionality methods that we evaluated did not discriminate true positives from true negatives using healthcare data as they seem to do using spontaneous report data.

A comparison of the empirical performance of methods for a risk identification system.

BACKGROUND: Observational healthcare data offer the potential to enable identification of risks of medical products, and the medical literature is replete with analyses that aim to accomplish this objective. A number of established analytic methods dominate the literature but their operating characteristics in real-world settings remain unknown. OBJECTIVES: To compare the performance of seven methods (new user cohort, case control, self-controlled case series, self-controlled cohort, disproportionality analysis, temporal pattern discovery, and longitudinal gamma poisson shrinker) as tools for risk identification in observational healthcare data. RESEARCH DESIGN: The experiment applied each method 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). MEASURES: Method performance was evaluated through Area Under the receiver operator characteristics Curve (AUC), bias, mean square error, and confidence interval coverage probability. RESULTS: Multiple methods offer strong predictive accuracy, with AUC > 0.70 achievable for all outcomes and databases with more than one analytical approach. Self-controlled methods (self-controlled case series, temporal pattern discovery, self-controlled cohort) had higher predictive accuracy than cohort and case-control methods across all databases and outcomes. Methods differed in the expected value and variance of the error distribution. All methods had lower coverage probability than the expected nominal properties. CONCLUSIONS: Observational healthcare data can inform risk identification of medical product effects on acute liver injury, acute myocardial infarction, acute renal failure and gastrointestinal bleeding. However, effect estimates from all methods require calibration to address inconsistency in method operating characteristics. Further empirical evaluation is required to gauge the generalizability of these findings to other databases and outcomes.

Evaluating the impact of database heterogeneity on observational study results.

Clinical studies that use observational databases to evaluate the effects of medical products have become commonplace. Such studies begin by selecting a particular database, a decision that published papers invariably report but do not discuss. Studies of the same issue in different databases, however, can and do generate different results, sometimes with strikingly different clinical implications. In this paper, we systematically study heterogeneity among databases, holding other study methods constant, by exploring relative risk estimates for 53 drug-outcome pairs and 2 widely used study designs (cohort studies and self-controlled case series) across 10 observational databases. When holding the study design constant, our analysis shows that estimated relative risks range from a statistically significant decreased risk to a statistically significant increased risk in 11 of 53 (21%) of drug-outcome pairs that use a cohort design and 19 of 53 (36%) of drug-outcome pairs that use a self-controlled case series design. This exceeds the proportion of pairs that were consistent across databases in both direction and statistical significance, which was 9 of 53 (17%) for cohort studies and 5 of 53 (9%) for self-controlled case series. Our findings show that clinical studies that use observational databases can be sensitive to the choice of database. More attention is needed to consider how the choice of data source may be affecting results.

Automated method for detecting increases in frequency of spontaneous adverse event reports over time.

A statistical methodology--focused on temporal change detection--was developed to highlight excursions from baseline spontaneous adverse event (AE) reporting. We used regression (both smooth trend and seasonal components) to model the time course of a drug's reports containing an AE, and then compared the sum of counts in the past 2 months with the fitted trend. The signaling threshold was tuned, using retrospective analysis, to yield acceptable sensitivity and specificity. The method may enhance pharmacovigilance by providing effective automated alerting of reporting aberrations when databases are small, when drugs have established safety profiles, and/or when product quality issues are of concern.

Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions.

OBJECTIVE: Data-mining algorithms that can produce accurate signals of potentially novel adverse drug reactions (ADRs) are a central component of pharmacovigilance. We propose a signal-detection strategy that combines the adverse event reporting system (AERS) of the Food and Drug Administration and electronic health records (EHRs) by requiring signaling in both sources. We claim that this approach leads to improved accuracy of signal detection when the goal is to produce a highly selective ranked set of candidate ADRs. MATERIALS AND METHODS: Our investigation was based on over 4 million AERS reports and information extracted from 1.2 million EHR narratives. Well-established methodologies were used to generate signals from each source. The study focused on ADRs related to three high-profile serious adverse reactions. A reference standard of over 600 established and plausible ADRs was created and used to evaluate the proposed approach against a comparator. RESULTS: The combined signaling system achieved a statistically significant large improvement over AERS (baseline) in the precision of top ranked signals. The average improvement ranged from 31% to almost threefold for different evaluation categories. Using this system, we identified a new association between the agent, rasburicase, and the adverse event, acute pancreatitis, which was supported by clinical review. CONCLUSIONS: The results provide promising initial evidence that combining AERS with EHRs via the framework of replicated signaling can improve the accuracy of signal detection for certain operating scenarios. The use of additional EHR data is required to further evaluate the capacity and limits of this system and to extend the generalizability of these results.

Adaptation of Bayesian data mining algorithms to longitudinal claims data: coxib safety as an example.

INTRODUCTION: Bayesian data mining methods have been used to evaluate drug safety signals from adverse event reporting systems and allow for evaluation of multiple endpoints that are not prespecified. Their adaptation for use with longitudinal data such as administrative claims has not been previously evaluated or validated. METHODS: In this pilot study, we evaluated the feasibility of adapting data mining methods using the empirical Bayes Multi-item Gamma Poisson Shrinkage (MGPS) algorithm to longitudinal administrative claims data. The Medicare Current Beneficiary Survey was used to identify a cohort of Medicare enrollees who were exposed to cyclooxygenase selective (coxib) or nonselective nonsteroidal anti-inflammatory drugs (NS-NSAIDs) from 1999 to 2003. Empirical Bayes MGPS algorithm was used to simultaneously evaluate 259 outcomes associated with current use of coxibs versus NS-NSAIDs while adjusting for key covariates and multiple comparisons. For comparison, a parallel analysis used traditional epidemiologic methods to evaluate the relationship between coxib versus NS-NSAID use and acute myocardial infarction, with the goal of establishing the concurrent validity of the data mining approach. RESULTS: Among 9431 Medicare beneficiaries using NSAIDs and considering all 259 possible outcomes, empirical Bayes MGPS identified an association between current celecoxib use and acute myocardial infarction (Empirical Bayes Geometric Mean ratio 1.91) but not other outcomes. Rofecoxib use was associated with acute cerebrovascular events (Empirical Bayes Geometric Mean ratio 1.85) and several other diagnoses that likely represented indications for the drug. Results from the analyses using traditional epidemiologic methods were similar and indicated that the data mining results were valid. DISCUSSION: Bayesian data mining methods seem useful to evaluate drug safety using administrative data. Further work will be needed to extend these findings to different types of drug exposures and to other claims databases.

Antipsychotics, glycemic disorders, and life-threatening diabetic events: a Bayesian data-mining analysis of the FDA adverse event reporting system (1968-2004).

BACKGROUND: This analysis compared diabetes-related adverse events associated with use of different antipsychotic agents. A disproportionality analysis of the US Food and Drug Administration (FDA) Adverse Event Reporting System (AERS) was performed. METHODS: Data from the FDA postmarketing AERS database (1968 through first quarter 2004) were evaluated. Drugs studied included aripiprazole, clozapine, haloperidol, olanzapine, quetiapine, risperidone, and ziprasidone. Fourteen Medical Dictionary for Regulatory Activities (MedDRA) Primary Terms (MPTs) were chosen to identify diabetes-related adverse events; 3 groupings into higher-level descriptive categories were also studied. Three methods of measuring drug-event associations were used: proportional reporting ratio, the empirical Bayes data-mining algorithm known as the Multi-Item Gamma Poisson Shrinker, and logistic regression (LR) analysis. Quantitative measures of association strength, with corresponding confidence intervals, between drugs and specified adverse events were computed and graphed. Some of the LR analyses were repeated separately for reports from patients under and over 45 years of age. Differences in association strength were declared statistically significant if the corresponding 90% confidence intervals did not overlap. RESULTS: Association with various glycemic events differed for different drugs. On average, the rankings of association strength agreed with the following ordering: low association, ziprasidone, aripiprazole, haloperidol, and risperidone; medium association, quetiapine; and strong association, clozapine and olanzapine. The median rank correlation between the above ordering and the 17 sets of LR coefficients (1 set for each glycemic event) was 93%. Many of the disproportionality measures were significantly different across drugs, and ratios of disproportionality factors of 5 or more were frequently observed. CONCLUSIONS: There are consistent and substantial differences between atypical antipsychotic drugs in the disproportionality reporting ratios relating to glycemic effects, especially life-threatening events, in the AERS database. The relative associational rankings of drugs are similar in reports from younger and older patients. These results agree with several other reports in the literature, do not support a "class effect" hypothesis, and provide a strong rationale for further studies to clarify the issue.

Signal detection methodologies to support effective safety management.

The increased focus on the safety of medical products, as well as the growing volume of available safety information, has created a need for objective quantitative approaches to supplement the medical review of individual case safety reports. Statistical algorithms can be used to identify trends and relationships in both clinical and postmarketing safety databases in support of safety signal detection. Powerful data visualization tools facilitate the medical review of the complex information generated by these methods. In addition, all these approaches need to be integrated into the daily practice of clinical safety and postmarketing pharmacovigilance.

Comparative performance of two quantitative safety signalling methods: implications for use in a pharmacovigilance department.

BACKGROUND AND OBJECTIVES: There is increasing interest in using disproportionality-based signal detection methods to support postmarketing safety surveillance activities. Two commonly used methods, empirical Bayes multi-item gamma Poisson shrinker (MGPS) and proportional reporting ratio (PRR), perform differently with respect to the number and types of signals detected. The goal of this study was to compare and analyse the performance characteristics of these two methods, to understand why they differ and to consider the practical implications of these differences for a large, industry-based pharmacovigilance department. METHODS: We compared the numbers and types of signals of disproportionate reporting (SDRs) obtained with MGPS and PRR using two postmarketing safety databases and a simulated database. We recorded signal counts and performed a qualitative comparison of the drug-event combinations signalled by the two methods as well as a sensitivity analysis to better understand how the thresholds commonly used for these methods impact their performance. RESULTS: PRR detected more SDRs than MGPS. We observed that MGPS is less subject to confounding by demographic factors because it employs stratification and is more stable than PRR when report counts are low. Simulation experiments performed using published empirical thresholds demonstrated that PRR detected false-positive signals at a rate of 1.1%, while MGPS did not detect any statistical false positives. In an attempt to separate the effect of choice of signal threshold from more fundamental methodological differences, we performed a series of experiments in which we modified the conventional threshold values for each method so that each method detected the same number of SDRs for the example drugs studied. This analysis, which provided quantitative examples of the relationship between the published thresholds for the two methods, demonstrates that the signalling criterion published for PRR has a higher signalling frequency than that published for MGPS. DISCUSSION AND CONCLUSION: The performance differences between the PRR and MGPS methods are related to (i) greater confounding by demographic factors with PRR; (ii) a higher tendency of PRR to detect false-positive signals when the number of reports is small; and (iii) the conventional thresholds that have been adapted for each method. PRR tends to be more 'sensitive' and less 'specific' than MGPS. A high-specificity disproportionality method, when used in conjunction with medical triage and investigation of critical medical events, may provide an efficient and robust approach to applying quantitative methods in routine postmarketing pharmacovigilance.

Contrast media and nephropathy: findings from systematic analysis and Food and Drug Administration reports of adverse effects.

CONTEXT: Recent studies suggest differences in the incidence of contrast-induced nephropathy (CIN) among contrast media (CM). OBJECTIVE: To determine whether there are significant differences among low-osmolality CM (LOCM) in the incidence of contrast-induced nephropathy (CIN), we reviewed published studies of CIN in renally impaired patients and conducted statistical data mining using databases of adverse events maintained by the U.S. Food and Drug Administration (FDA). DATA SOURCES: A systematic literature search was performed for prospective, controlled, English language studies published in peer-reviewed journals that reported CIN rates in renally impaired patients after a specific LOCM. Databases searched were EMBASE, MEDLINE, Biosis Previews, Derwent Drug File, Pascal, and SciScearch Cited Ref Sci. For the FDA analysis, we used the SRS and AERS databases. DATA SELECTION: Twenty-two studies reporting data in 3112 patients with renal impairment met the inclusion criteria. Most studies reported on the use of a pharmacologic intervention to prevent CIN. From the FDA databases, we evaluated 18 adverse event terms associated with renal injury or dysfunction after CM use. DATA EXTRACTION: Data from 22 studies were entered into a database. A meta-regression analysis using a mixed effect model was performed. CM effect was adjusted by the following covariates: baseline patient characteristics (mean age, gender distribution) and risk factors (prevalence of diabetes mellitus, degree of renal impairment, CM volume), and the use of prophylactic drug treatments. Multiple disproportionality analyses (adjusted odds ratio, adjusted empirical Bayesian estimate, or Bayesian logistic regression) were performed on the FDA databases to estimate associations between 4 CM and 18 AE terms related to CIN. DATA SYNTHESIS: Systematic analysis of clinical trials suggest the highest incidence of CIN occurs in patients receiving iohexol and the lowest incidence in patients receiving iopamidol, even when corrected for other CIN risk factors. Statistical data mining of FDA data also showed the highest association of CIN for iohexol and the lowest for iopamidol. CONCLUSIONS: The risk of CIN was higher in patients receiving iohexol compared with patients receiving iopamidol. No significant differences were found comparing iohexol to other LOCMs, including iodixanol.

Association of asthma therapy and Churg-Strauss syndrome: an analysis of postmarketing surveillance data.

BACKGROUND: Churg-Strauss syndrome (CSS), also known as allergic granulomatous angiitis (AGA), is a rare vasculitis that occurs in patients with bronchial asthma. The nature of the association of CSS with various asthma therapies is unclear. OBJECTIVE: This study investigated the associations of different multidrug asthma therapy regimens and the reporting of AGA (the preferred code for CSS in the coding dictionary for the Adverse Event Reporting System [AERS]) by applying an iterative method of disproportionally analysis to th AERS database maintained by the US Food and Drug Administration. METHODS: The public-release version of the AERS database was used to identify reports of AGA in patients receiving asthma therapy. Reporting of AGA was examined using iterative disproportionality methods in patients receiving > or =1 of the following drug classes: inhaled corticosteroid (ICS), leukotriene receptor antagonist (LTRA), short-acting beta(2)-agonist (SABA), or long-acting beta(2)-agonist (LABA). The Bayesian data-mining algorithm known as the multi-item gamma poisson shrinker was used to determine the relative reporting rates by calculation of the empirical Bayes geometric mean (EBGM) and its 90% CI (EB05 = lower limit and EB95 = upper limit) for each drug. Subset analyses were performed for each drug with different medication combinations to differentiate the relative reporting of AGA for each. RESULTS: A strong association was found between LTRA use and AGA (EBGM = 104.0, EB05 = 95.0, EB95 = 113.8) that persisted with all combinations of therapy studied. AGA was also associated with the ICS, SABA and LABA classes (EBGM values of 27.8, 14.6 and 40.4, respectively). However, the latter associations were mostly dependent on the presence of concurrent LTRA and, to a lesser extemt, oral corticosteroid therapy and became negligible (ie, EB05 < 2) for patients who were not receiving these concurrent treatments. CONCLUSIONS: Differences based on relative reporting were observed in the patterns of association of AGA with LTRA, ICS, and beta(2)-agonist therapies. A strong association between LTRA use and AGA was present regardless of the use of other asthma drugs.