Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System

DuMouchel, William

doi:10.1080/00031305.1999.10474456

Cited by 438 publications

(530 citation statements)

References 14 publications

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“…MGPS is an innovative data-mining algorithm developed by William DuMouchel which introduces the concept of refining the relative reporting ratio calculation by using Bayesian statistics. [19][20][21] The basic assumption behind MGPS is that each observed count (N; drug/vaccine-event combination) is taken from a Poisson distribution with unknown mean (μ), with an interest center on the ratio λ which equals μ divided by E (the expected count estimated by assuming that the count of all reports for the specific drug/vaccine and the count of all reports for the specific event are independent; Table 1). The essential Bayesian contribution supposes that each λ is drawn from a common prior assumed to be a mixture of two g distributions.…”

Section: Methodsmentioning

confidence: 99%

Quantitative signal detection for vaccines: Effects of stratification, background and masking on GlaxoSmithKline’s spontaneous reports database

Zeinoun¹,

Seifert²,

Verstraeten³

2009

Human Vaccines

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Disproportionality analyses are increasingly gaining acceptance as signal detection tools for drug adverse events. Their application to vaccine adverse events has not been well evaluated. Disproportionality analyses based on the Multi-Item Gamma Poisson Shrinkage principle (MGPS) were applied to spontaneous adverse events reports for five vaccines with different reporting profiles. Sensitivity analyses were used to assess the potential impact of changing key parameters. We used the Company's in-house spontaneous adverse events database. We evaluated the impact of stratification, the comparator dataset and the potential for masking. We conducted a semi-quantitative assessment by comparing the changes in the disproportionality scores and the number of vaccine-event pairs that exceeded an arbitrary threshold as a measure of the impact of any of these choices. The results show that stratification by age and region has a significant impact. The effect of the comparator dataset was dependent on the vaccine being evaluated. The potential for a masking effect was only weakly noticeable. In conclusion, we opt to start with a conservative approach in which we will supplement our primary stratification against the vaccine database with unstratified analyses as well as analyses against the entire database. We recommend that similar studies be performed before introducing disproportionality analyses to a new vaccine adverse events database.

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Section: Methodsmentioning

confidence: 99%

Quantitative signal detection for vaccines: Effects of stratification, background and masking on GlaxoSmithKline’s spontaneous reports database

Zeinoun¹,

Seifert²,

Verstraeten³

2009

Human Vaccines

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show abstract

“…Among the former, the Reporting Odds Ratio (ROR) is applied by the Netherlands Pharmacovigilance Centre Lareb, whereas the Proportional Reporting Ratio (PRR) was first used by Evans et al [35]. Bayesian methods such as Multi-item Gamma Poisson Shrinker (MGPS) [36] and Bayesian Confidence Propagation Neural network (BCPN) [37] are based on Bayes' law to estimate the probability (posterior probability) that the suspected event occurs given the use of suspect drug.…”

Section: The Use Of Dmasmentioning

confidence: 99%

Data Mining Techniques in Pharmacovigilance: Analysis of the Publicly Accessible FDA Adverse Event Reporting System (AERS)

Poluzzi¹,

Raschi²,

Piccinni³

et al. 2012

Data Mining Applications in Engineering and Medicine

156

195

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Background: Acute kidney injury (AKI) is associated with signi cant increases in short-and long-term morbidity and mortality. Drug-induced AKI is a major concern in the present healthcare system. Our spontaneous reporting system (SRS) analysis assessed links between AKIs, along with patients' age, as healthcare-associated risks and administered anti-infectives. We also generated anti-infectives-related AKI-onset pro les. Method: We calculated adjusted reporting odds ratios (RORs) for reports of anti-infectives-related AKIs (per Medical Dictionary for Regulatory Activities) in the Japanese Adverse Drug Event Report database and evaluated associations between anti-infectives and age by association rule mining. We evaluated time-to-onset data and hazard types using the Weibull parameter. Results: Among 534,688 reports (submission period: April 2004-June 2018), there were 21,727 AKI events. Anti-infective treatments including glycopeptide antibacterials, uoroquinolones, third-generation cephalosporins, triazole derivatives, and carbapenems were associated with 596, 494, 341, 315, and 313 AKI incidences, respectively. Adjusted RORs of anti-infectives-related AKIs increased among older patients and were higher in anti-infective combination therapies [anti-infectives, ≥ 2; ROR, 2.75 (2.56-2.95)] than in monotherapies [ROR, 1.52 (1.45-1.61)]. In association rule mining, the number of anti-infectives and age were associated with anti-infectives-related AKI lift values (as consequent). Moreover, 48.1% of AKIs occurred within 5 days (median, 5.0 days) of anti-infective therapy initiation. Conclusion: Thus, adjusted RORs derived from our new SRS analysis indicate potential AKI risks linked to age and number of administered anti-infectives.

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“…However, the Probability Ratio is very volatile when the expected value is small, which makes it favor the rare combinations rather than significant trends in the data. In order to solve the problem, people use shrinkage [1], [3], [10] to regularize and reduce the volatility of a measure by trading a bias to no correlation for decreased variance. Specifically, we add a continuity correction number to both nominator and denominator.…”

Section: ) Two-by-two Contingency Tablementioning

confidence: 99%

Adverse Drug Effect Detection

Duan

Khoshneshin

Street

et al. 2013

IEEE J. Biomed. Health Inform.

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Abstract-Large collections of electronic patient records provide abundant but under-explored information on the real-world use of medicines. Although they are maintained for patient administration, they provide a broad range of clinical information for data analysis. One growing interest is drug safety signal detection from these longitudinal observational data. In this paper, we proposed two novel algorithms-a likelihood ratio model and a Bayesian network model-for adverse drug effect discovery. Although the performance of these two algorithms is comparable to the state-of-the-art algorithm, Bayesian confidence propagation neural network, the combination of three works better due to their diversity in solutions. Since the actual adverse drug effects on a given dataset cannot be absolutely determined, we make use of the simulated OMOP dataset constructed with the predefined adverse drug effects to evaluate our methods. Experimental results show the usefulness of the proposed pattern discovery method on the simulated OMOP dataset by improving the standard baseline algorithm-chi-square-by 23.83%.

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Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System

Cited by 438 publications

References 14 publications

Quantitative signal detection for vaccines: Effects of stratification, background and masking on GlaxoSmithKline’s spontaneous reports database

Quantitative signal detection for vaccines: Effects of stratification, background and masking on GlaxoSmithKline’s spontaneous reports database

Data Mining Techniques in Pharmacovigilance: Analysis of the Publicly Accessible FDA Adverse Event Reporting System (AERS)

Adverse Drug Effect Detection

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