Three‐Component Mixture Model‐Based Adverse Drug Event Signal Detection for the Adverse Event Reporting System

Zhang, Pengyue; Li, Meng; Chiang, Chien Wei; Wang, Lei; Xiang, Yang; Cheng, Lijun; Feng, Weixing; Schleyer, Titus; Quinney, Sara K.; Wu, Hui; Zeng, Donglin; Li, Lang

doi:10.1002/psp4.12294

Cited by 9 publications

(22 citation statements)

References 26 publications

(60 reference statements)

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“…After filtering the report frequencies of drug‐drug combinations (eg, >50), we identified 81 312 drug‐drug combinations generated by 1061 drugs. Furthermore, we selected four primary ADEs for analysis including delirium, myopathy, neuropathy, and skin pigmentation disorder . Our final dataset contained 256 887 drug‐drug‐ADE combinations which were formed from four ADEs and 1061 drugs (Figure ).…”

Section: Methodsmentioning

confidence: 99%

“…Similarly, the estimations for the odds of the ADE in the absence of one of D1 and D2 are Odds 10 = r 10 1 − r 10 = exp(̂0 +̂1), and…”

Section: Logistic Regression-based Expected Frequency For Drug-drug-amentioning

confidence: 99%

“…Alternatively, if only one drug has ADE risk and the other one does not, we define it as DDI-NPIRR, which increases the ADE risk when two drugs are coprescribed. In terms of reporting rate, DDI-NPIRR has the pattern r 11 > r 10 > r 00 ≥ r 01 or r 11 > r 01 > r 00 ≥ r 10 . We compared the top-100 DDI signals between two methods in Figure 3: PS-3CMM and Ω shrinkage.…”

Section: Performance Comparisons Between Ps-3cmm and Shrinkagementioning

confidence: 99%

“…Though multiple regression approach is not necessarily limited by the sample size challenge, techniques for properly selecting confounding variables are still underdeveloped . False positive control is another significant challenge in detecting DDI signals from hundreds of thousands drug pairs . False discovery rate (FDR) is more practical than traditional hypothesis testing techniques (eg, P ‐value) for large‐scale DDI signals detection.…”

Section: Introductionmentioning

confidence: 99%

“…6 Well-known DPAs include the Information Component (IC), 7 IC with Dirichlet prior, 8 empirical Bayes geometric mean (EBGM) also known as the multigamma Poisson shrinkage (MGPS), 9 and the three-component mixture model (3CMM). 10 On the other hand, the likelihood ratio test (LRT) and its extension zero-inflated Poisson model-based LRT (ZIP-LRT) are able to test the associations between an ADE and many drugs. 11,12 The DPA method has also been used for investigating the DDI-induced ADEs by comparing the observed report frequency of a drug-drug-ADE combination to its baseline frequency assuming no interactions between two drugs.…”

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confidence: 99%

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Propensity score‐adjusted three‐component mixture model for drug‐drug interaction data mining in FDA Adverse Event Reporting System

Wang

Feng

et al. 2019

Statistics in Medicine

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With increasing trend of polypharmacy, drug‐drug interaction (DDI)‐induced adverse drug events (ADEs) are considered as a major challenge for clinical practice. As premarketing clinical trials usually have stringent inclusion/exclusion criteria, limited comedication data capture and often times small sample size have limited values in study DDIs. On the other hand, ADE reports collected by spontaneous reporting system (SRS) become an important source for DDI studies. There are two major challenges in detecting DDI signals from SRS: confounding bias and false positive rate. In this article, we propose a novel approach, propensity score‐adjusted three‐component mixture model (PS‐3CMM). This model can simultaneously adjust for confounding bias and estimate false discovery rate for all drug‐drug‐ADE combinations in FDA Adverse Event Reporting System (FAERS), which is a preeminent SRS database. In simulation studies, PS‐3CMM performs better in detecting true DDIs comparing to the existing approach. It is more sensitive in selecting the DDI signals that have nonpositive individual drug relative ADE risk (NPIRR). The application of PS‐3CMM is illustrated in analyzing the FAERS database. Compared to the existing approaches, PS‐3CMM prioritizes DDI signals differently. PS‐3CMM gives high priorities to DDI signals that have NPIRR. Both simulation studies and FAERS data analysis conclude that our new PS‐3CMM is a new method that is complement to the existing DDI signal detection methods.

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Section: Methodsmentioning

confidence: 99%

“…Similarly, the estimations for the odds of the ADE in the absence of one of D1 and D2 are Odds 10 = r 10 1 − r 10 = exp(̂0 +̂1), and…”

Section: Logistic Regression-based Expected Frequency For Drug-drug-amentioning

confidence: 99%

Section: Performance Comparisons Between Ps-3cmm and Shrinkagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Propensity score‐adjusted three‐component mixture model for drug‐drug interaction data mining in FDA Adverse Event Reporting System

Wang

Feng

et al. 2019

Statistics in Medicine

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Random control selection for conducting high‐throughput adverse drug events screening using large‐scale longitudinal health data

Chiang

Zhang

Donneyong

et al. 2021

CPT Pharmacom & Syst Pharma

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Case-control design based high-throughput pharmacoinformatics study using large-scale longitudinal health data is able to detect new adverse drug events (ADEs) signals. Existing control selection approaches for case-control design including dynamic/super control selection approach. Dynamic/super control selection approach requires all individuals to be evaluated at all ADE case index dates, as the individuals' eligibilities as control depend on ADE/enrolment history. Thus, using large-scale longitudinal health data, dynamic/super control selection approach requires extraordinarly high computational time. We proposed a random control selection approach in which ADE case index dates were matched by randomly generated control index dates. Random control selection approach doesn't depend on ADE/enrolment history. It is able to significantly reduce computational time to prepare case-control data sets, as it requires all individuals to be evaluated only once. We compared the performance metrics of all control selection approaches using two large-scale longitudinal health data and a drug-ADE gold standard including 399 drug-ADE pairs. The F-scores for random control selection approach were between 0.586 -0.600 compared to between 0.545 -0.562 for dynamic/super control selection approaches. Random control selection approach was approximately 1,000 times faster than dynamic/super control selection approach on preparing case-control data sets. With large-scale longitudinal health data, case-control design based pharmacoinformatics study using random control selection is able to generate comparable ADE signals than the existing control selection approaches. Random control selection approach also significantly reduces computational time to prepare the case-control data sets.

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A Precision Mixture Risk Model to Identify Adverse Drug Events in Subpopulations Using a Case‐Crossover Design

Shi,

Eadon,

Chen

et al. 2024

Statistics in Medicine

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Despite the success of pharmacovigilance studies in detecting signals of adverse drug events (ADEs) from real‐world data, the risks of ADEs in subpopulations warrant increased scrutiny to prevent them in vulnerable individuals. Recently, the case‐crossover design has been implemented to leverage large‐scale administrative claims data for ADE detection, while controlling both observed confounding effects and short‐term fixed unobserved confounding effects. Additionally, as the case‐crossover design only includes cases, subpopulations can be conveniently derived. In this manuscript, we propose a precision mixture risk model (PMRM) to identify ADE signals from subpopulations under the case‐crossover design. The proposed model is able to identify signals from all ADE‐subpopulation‐drug combinations, while controlling for false discovery rate (FDR) and confounding effects. We applied the PMRM to an administrative claims data. We identified ADE signals in subpopulations defined by demographic variables, comorbidities, and detailed diagnosis codes. Interestingly, certain drugs were associated with a higher risk of ADE only in subpopulations, while these drugs had a neutral association with ADE in the general population. Additionally, the PMRM could control FDR at a desired level and had a higher probability to detect true ADE signals than the widely used McNemar's test. In conclusion, the PMRM is able to identify subpopulation‐specific ADE signals from a tremendous number of ADE‐subpopulation‐drug combinations, while controlling for both FDR and confounding effects.

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Three‐Component Mixture Model‐Based Adverse Drug Event Signal Detection for the Adverse Event Reporting System

Cited by 9 publications

References 26 publications

Propensity score‐adjusted three‐component mixture model for drug‐drug interaction data mining in FDA Adverse Event Reporting System

Propensity score‐adjusted three‐component mixture model for drug‐drug interaction data mining in FDA Adverse Event Reporting System

Random control selection for conducting high‐throughput adverse drug events screening using large‐scale longitudinal health data

A Precision Mixture Risk Model to Identify Adverse Drug Events in Subpopulations Using a Case‐Crossover Design

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