Bayesian Methods in Pharmacovigilance*

Madigan, David; Ryan, Patrick B.; Simpson, Shawn E.; Zorych, Ivan

doi:10.1093/acprof:oso/9780199694587.003.0014

Cited by 23 publications

(24 citation statements)

References 40 publications

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“…Our constant eras are intervals of time where patients remain on the same combination of medication. For example, consider a patient who takes drug A from July 5, 2009 through July 20, 2009 and drug B from July [10][11][12][13][14][15][16][17]2009. Three distinct drug eras emerge: one era from July 5-9; another from July 10-17; and the last era from July 18-20.…”

Section: Sccs Frameworkmentioning

confidence: 99%

Hierarchical models for multiple, rare outcomes using massive observational healthcare databases

Shaddox

Ryan

Schuemie

et al. 2016

Statistical Analysis

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Clinical trials often lack power to identify rare adverse drug events (ADEs) and therefore cannot address the threat rare ADEs pose, motivating the need for new ADE detection techniques. Emerging national patient claims and electronic health record databases have inspired post-approval early detection methods like the Bayesian self-controlled case series (BSCCS) regression model. Existing BSCCS models do not account for multiple outcomes, where pathology may be shared across different ADEs. We integrate a pathology hierarchy into the BSCCS model by developing a novel informative hierarchical prior linking outcome-specific effects. Considering shared pathology drastically increases the dimensionality of the already massive models in this field. We develop an efficient method for coping with the dimensionality expansion by reducing the hierarchical model to a form amenable to existing tools. Through a synthetic study we demonstrate decreased bias in risk estimates for drugs when using conditions with different true risk and unequal prevalence. We also examine observational data from the MarketScan Lab Results dataset, exposing the bias that results from aggregating outcomes, as previously employed to estimate risk trends of warfarin and dabigatran for intracranial hemorrhage and gastrointestinal bleeding. We further investigate the limits of our approach by using extremely rare conditions. This research demonstrates that analyzing multiple outcomes simultaneously is feasible at scale and beneficial.

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Section: Sccs Frameworkmentioning

confidence: 99%

Hierarchical models for multiple, rare outcomes using massive observational healthcare databases

Shaddox

Ryan

Schuemie

et al. 2016

Statistical Analysis

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“…In addition, [23] applied the Bayesian method in estimated the drug safety problems. The results described that Bayesian method is useful in analysis pharmaceutical and health industries.…”

Section: Literature Reviewmentioning

confidence: 99%

A study of finite mixture model: Bayesian approach on financial time series data

Phoong

Ismail

2014

AIP Conference Proceedings

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“…In this article, we focus on updating Bayesian probabilities using estimates generated from two specific but rather different epidemiologic designs: inception cohort (Ray 2003) and self-controlled case series (SCCS) (Whitaker et al 2006;Whitaker 2008;Madigan et al 2010;Maclure et al 2012). The inception cohort design is commonly used in drug safety and comparative effectiveness research to compare newly exposed patients to two alternative treatments, with confounding adjustment performed using baseline covariates at the time of drug initiation (Schneeweiss 2010;Johnson et al 2013).…”

Section: Designsmentioning

confidence: 99%

Learning From Epidemiology: Interpreting Observational Database Studies for the Effects of Medical Products

Ryan

Suchard²,

Schuemie

et al. 2013

Statistics in Biopharmaceutical Research

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Causal assessment of adverse effects continues to evolve through the medical product lifecycle and requires clinical judgment to integrate evidence from multiple sources, including observational studies. This study describes a probabilistic framework quantifying how much can be learned from an epidemiological study, and how it varies by study design, database, or prior beliefs. We integrate new observational evidence with existing clinical knowledge by estimating the probability that a drug-outcome pair represents a true association, as a function of prior belief and estimated strength of association from an observational study. Incident user cohort with propensity score adjustment and self-controlled case series designs were applied in an automated fashion to 53 drug-outcome associations across a network of nine disparate databases covering over 100 million lives of patient-level data. When applying a incident user design to a large claims database for a drug with moderate sample, the performance of these method suggests that an observed relative risk of less than 2 does not substantially shift prior expectations and that a relative risk of greater than 5 is needed to revise a 10% prior belief in a true association to have a greater than 90% posterior probability. Study design, database, and sample size can substantially impact the predictive performance of observational results. This Bayesian framework offers a conceptual approach to interpreting observational database studies, which, with further data to fit the model, offers the potential to become a practical tool for direct use by clinicians and the broader research commnunity.

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Bayesian Methods in Pharmacovigilance*

Cited by 23 publications

References 40 publications

Hierarchical models for multiple, rare outcomes using massive observational healthcare databases

Hierarchical models for multiple, rare outcomes using massive observational healthcare databases

A study of finite mixture model: Bayesian approach on financial time series data

Learning From Epidemiology: Interpreting Observational Database Studies for the Effects of Medical Products

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