Signal detection in FDA AERS database using Dirichlet process

Hu, Na; Huang, Lan; Tiwari, Ram C.

doi:10.1002/sim.6510

Cited by 14 publications

(16 citation statements)

References 34 publications

(48 reference statements)

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“…The sB method defines the prior distribution of τ ij , where τ ij is the ratio of the mean of the observed frequency distribution and the expected frequency as defined in MGPS, as a Gamma ( a , a ) distribution with identical scale and shape parameters a ∈(0,1). In addition, Hu et al introduce a hierarchical Bayes method that generates the prior for τ ij from a Poisson‐Dirichlet process, providing more flexibility. Norén et al developed a new IC method by using a new shrinkage to the observed‐to‐expected ratio that consists of adding a constant in both numerator and denominator and discuss approaches to use stratification.…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

An evaluation of statistical approaches to postmarketing surveillance

Ding

Markatou

Ball

2020

Statistics in Medicine

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Safety of medical products presents a serious concern worldwide. Surveillance systems of postmarket medical products have been established for continual monitoring of adverse events (AEs) in many countries, and the proliferation of electronic health record systems further facilitates continual monitoring for AEs. We review existing statistical methods for signal detection that are mostly in use in postmarketing safety surveillance of spontaneously reported AEs and we study their performance characteristics by simulation. We compare those with the likelihood ratio test (LRT) method (appropriately modified for use in pharmacovigilance) and use three different methods to generate data (AE based, drug based, and a modification of the method of Ahmed et al). Performance metrics include type I error, power, sensitivity, and false discovery rate, among others. The results show superior performance of the LRT method in almost all simulation experiments. An application to the FDA Adverse Event Reporting System database is illustrated using rhabdomyolysis‐related preferred terms reported to FDA during the third‐quarter of 2014 to the first‐quarter of 2017 for statin drugs. We present a critical discussion and recommendations for use of these methods.

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Section: Discussion and Recommendationsmentioning

confidence: 99%

An evaluation of statistical approaches to postmarketing surveillance

Ding

Markatou

Ball

2020

Statistics in Medicine

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“…ROR, PRR, IC, and LRT are frequentist methods, while GPS, BCPNN, the new IC method, and sB are Bayesian methods [ 10 ]. Some studies suggest that Bayesian methods, such as multi-item gamma Poisson shrinker (MGPS) and BCPNN, outperform frequentist methods such as PRR [ 2 , 3 , 6 , 7 , 11 ]. Other studies showed that the sB method performed better than BCPNN and PRR [ 9 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…Some studies suggest that Bayesian methods, such as multi-item gamma Poisson shrinker (MGPS) and BCPNN, outperform frequentist methods such as PRR [ 2 , 3 , 6 , 7 , 11 ]. Other studies showed that the sB method performed better than BCPNN and PRR [ 9 , 11 ]. Unlike other methods, the GPS method needs to estimate hyperparameters of the prior distribution using the whole data.…”

Section: Introductionmentioning

confidence: 99%

“…Clinical information related to adverse drug reactions is generally coded using medical dictionaries such as the World Health Organization Adverse Reaction Terminology (WHO-ART) and the Medical Dictionary for Regulatory Activities (MedDRA), which have a hierarchical structure. There are several studies comparing the performance of disproportionality methods in a usual database format with AEs and drug combination data [ 2 , 3 , 6 , 7 , 9 , 11 , 12 , 15 ]. However, it is not clear which method performs better than the others when AEs are classified into a hierarchical structure.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Data Mining Methods for the Signal Detection of Adverse Drug Events with a Hierarchical Structure in Postmarketing Surveillance

Park

Jung

Heo

et al. 2020

Life

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There are several different proposed data mining methods for the postmarketing surveillance of drug safety. Adverse events are often classified into a hierarchical structure. Our objective was to compare the performance of several of these different data mining methods for adverse drug events data with a hierarchical structure. We generated datasets based on the World Health Organization’s Adverse Reaction Terminology (WHO-ART) hierarchical structure. We evaluated different data mining methods for signal detection, including several frequentist methods such as reporting odds ratio (ROR), proportional reporting ratio (PRR), information component (IC), the likelihood ratio test-based method (LRT), and Bayesian methods such as gamma Poisson shrinker (GPS), Bayesian confidence propagating neural network (BCPNN), the new IC method, and the simplified Bayesian method (sB), as well as the tree-based scan statistic through an extensive simulation study. We also applied the methods to real data on two diabetes drugs, voglibose and acarbose, from the Korea Adverse event reporting system. Only the tree-based scan statistic method maintained the type I error rate at the desired level. Likelihood ratio test-based methods and Bayesian methods tended to be more conservative than other methods in the simulation study and detected fewer signals in the real data example. No method was superior to the others in terms of the statistical power and sensitivity of detecting true signals. It is recommended that those conducting drug‒adverse event surveillance use not just one method, but make a decision based on several methods.

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“…Data from such databases include many different drugs and many different types of AEs across different SOCs. Various methods have been developed for safety signal detection (or detection of drug‐AE pairs) using data from SR; for example, proportional reporting ratios (Evans et al, ), reporting odds ratios (Rothman et al, ), the likelihood ratio tests (Huang et al, , , , ; Nam et al, ; Zhao et al, ), and Bayesian methods (Bate et al, ; DuMouchel, ; DuMouchel and Pregibon, ; Norén et al, ; Hu et al, ).…”

Section: Introductionmentioning

confidence: 99%

Efficient Methods for Signal Detection From Correlated Adverse Events in Clinical Trials

et al. 2019

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It is an important and yet challenging task to identify true signals from many adverse events that may be reported during the course of a clinical trial. One unique feature of drug safety data from clinical trials, unlike data from post-marketing spontaneous reporting, is that many types of adverse events are reported by only very few patients leading to rare events. Due to the limited study size, the -values of testing whether the rate is higher in the treatment group across all types of adverse events are in general not uniformly distributed under the null hypothesis that there is no difference between the treatment group and the placebo group. A consequence is that typically fewer than 100 percent of the hypotheses are rejected under the null at the nominal significance level of . The other challenge is multiplicity control. Adverse events from the same body system may be correlated. There may also be correlations between adverse events from different body systems. To tackle these challenging issues, we develop Monte-Carlo-based methods for the signal identification from patientreported adverse events in clinical trials. The proposed methodologies account for the rare events and arbitrary correlation structures among adverse events within and/or between body systems. Extensive simulation studies demonstrate that the proposed method can accurately control the family-wise error rate and is more powerful than existing methods under many practical situations. Application to two real examples is provided. KEYWORDSfamily-wise error rate, multiplier bootstrap, permutation test, rademacher sequence, score test 1 Biometrics. 2019;1-9 wileyonlinelibrary.com/journal/biom

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Signal detection in FDA AERS database using Dirichlet process

Cited by 14 publications

References 34 publications

An evaluation of statistical approaches to postmarketing surveillance

An evaluation of statistical approaches to postmarketing surveillance

Comparison of Data Mining Methods for the Signal Detection of Adverse Drug Events with a Hierarchical Structure in Postmarketing Surveillance

Efficient Methods for Signal Detection From Correlated Adverse Events in Clinical Trials

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