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An MCEM Framework for Drug Safety Signal Detection and Combination from Heterogeneous Real World Evidence.

ABSTRACT: Delayed drug safety insights can impact patients, pharmaceutical companies, and the whole society. Post-market drug safety surveillance plays a critical role in providing drug safety insights, where real world evidence such as spontaneous reporting systems (SRS) and a series of disproportional analysis serve as a cornerstone of proactive and predictive drug safety surveillance. However, they still face several challenges including concomitant drugs confounders, rare adverse drug reaction (ADR) detection, data bias, and the under-reporting issue. In this paper, we are developing a new framework that detects improved drug safety signals from multiple data sources via Monte Carlo Expectation-Maximization (MCEM) and signal combination. In MCEM procedure, we propose a new sampling approach to generate more accurate SRS signals for each ADR through iteratively down-weighting their associations with irrelevant drugs in case reports. While in signal combination step, we adopt Bayesian hierarchical model and propose a new summary statistic such that SRS signals can be combined with signals derived from other observational health data allowing for related signals to borrow statistical support with adjustment of data reliability. They combined effectively alleviate the concomitant confounders, data bias, rare ADR and under-reporting issues. Experimental results demonstrated the effectiveness and usefulness of the proposed framework.

SUBMITTER: Xiao C

PROVIDER: S-EPMC5789130 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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An MCEM Framework for Drug Safety Signal Detection and Combination from Heterogeneous Real World Evidence.

Xiao Cao C Li Ying Y Baytas Inci M IM Zhou Jiayu J Wang Fei F

Scientific reports 20180129 1

Delayed drug safety insights can impact patients, pharmaceutical companies, and the whole society. Post-market drug safety surveillance plays a critical role in providing drug safety insights, where real world evidence such as spontaneous reporting systems (SRS) and a series of disproportional analysis serve as a cornerstone of proactive and predictive drug safety surveillance. However, they still face several challenges including concomitant drugs confounders, rare adverse drug reaction (ADR) d ...[more]

PMID: 29379048

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Project description:IntroductionFor signal detection studies investigating either drug safety or method evaluation, the choice of drug-outcome pairs needs to be tailored to the planned study design and vice versa. While this is well understood in hypothesis-testing epidemiology, it should be as important in signal detection, but this has not widely been considered. There is a need for a taxonomy framework to provide guidance and a systematic reproducible approach to the selection of appropriate drugs and outcomes for signal detection studies either investigating drug safety or assessing method performance using real-world data.ObjectiveThe aim was to design a general framework for the selection of appropriate drugs and outcomes for signal detection studies given a study design of interest. As a motivating example, we illustrate how the framework is applied to build a reference set for a study aiming to assess the performance of the self-controlled case series with active comparators.MethodsWe reviewed criteria presented in two published studies which aimed to provide practical advice for choosing the appropriate signal evaluation methodology, and assessed their relevance for signal detection. Further characteristics specific to signal detection were added. The final framework is based on: the application of study design requirements, the database(s) of interest, and the clinical importance of the drug(s) and outcome(s) under consideration. This structure was applied by selecting drug-outcome pairs as a reference set (i.e. list of drug-outcome pairs classified as positive or negative controls) for which the method is expected to work well for a signal detection study aiming to assess the performance of self-controlled case series. Eight criteria were used, related to the application of self-controlled case series assumptions, choice of active comparators, coverage in the database of interest and clinical importance of the outcomes.ResultsAfter application of the framework, two classes of antibiotics (seven drugs) were selected for the study, and 28 outcomes from all organ classes were chosen from the drug labels, out of the 273 investigated. In total, this corresponds to 104 positive controls (drug-outcome pairs) and 58 negative controls.ConclusionsWe proposed and applied a framework for the selection of drugs and outcomes for both drug safety signal detection and method assessment used in signal detection to optimise their performance given a study design. This framework will eliminate part of the bias relating to drugs and outcomes not being suited to the method or database. The main difficulty lies in the choice of the criteria and their application to ensure systematic selection, especially as some information remains unknown in signal detection, and clinical judgement was needed on occasions. The same framework could be adapted for other methods.

Project description:BACKGROUND:The promise of real-world evidence (RWE) is especially relevant to pediatrics, where medicines prescribed for children are often used without evidence derived from randomized clinical trials. OBJECTIVES:The aim of this systematic review was to describe the state of RWE in pediatrics by identifying observational studies published during 2016 that used RWE to assess medication safety or effectiveness in children. METHODS:An electronic search of PubMed was combined with an extended search of references within systematic reviews and expert suggestions. Studies were included if they reported on an infant or child under 18 years with exposure to medications; assessed safety or effectiveness; specified a comparison or control group, and were published in English in 2016. Data extraction was conducted by one team member using a standardized form and reviewed by a second team member. Study quality was assessed using the GRACE checklist for rating the quality of observational studies. RESULTS:After removing duplicates, 915 citations were screened and 29 studies met the eligibility criteria. Most of the eligible studies relied on primary data collection or chart review at a single institution and did not use the growing number of administrative or electronic health record databases available. One-quarter of the studies did not use well-established statistical methods to control for confounders. No single disease group or medication predominated, and age groups ranged from infants to adolescents. CONCLUSIONS:A small body of observational studies published in 2016 were categorized by the study team as using real-world data to assess medication safety or effectiveness in children. Studies varied in age groups, diseases or conditions, and methods, and may not have fully met the FDA definition of RWE. Our review indicates that the use of RWE is not fully developed in pediatrics, and suggests an opportunity to further develop capabilities and more fully leverage administrative and electronic health record databases to study medication safety and effectiveness in children. Our systematic review appears generalizable to pediatrics broadly, and documents that the high level of activity in RWE in general has had less of an impact on pediatrics.

Dataset Information

An MCEM Framework for Drug Safety Signal Detection and Combination from Heterogeneous Real World Evidence.

Publications

An MCEM Framework for Drug Safety Signal Detection and Combination from Heterogeneous Real World Evidence.

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