ArticleDrug safety2013
Empirical performance of LGPS and LEOPARD: lessons for developing a risk identification and analysis system.
Article in Drug safety, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Methods for drug safety signal detection using routinely collected observational electronic health care data: A systematic review.Pharmacoepidemiology and drug safety · 2023Pooled it
- Foundational model aided automatic high-throughput drug screening using self-controlled cohort study.medRxiv : the preprint server for health sciences · 2024Article
- The State of Use and Utility of Negative Controls in Pharmacoepidemiologic Studies.American journal of epidemiology · 2024Review
- Challenges and Opportunities of Real-World Data: Statistical Analysis Plan for the Optimise:MS Multicenter Prospective Cohort Pharmacovigilance Study.Frontiers in neurology · 2022Article
- How Confident Are We about Observational Findings in Healthcare: A Benchmark Study.Harvard data science review · 2020Article
- Pharmacovigilance via Baseline Regularization with Large-Scale Longitudinal Observational Data.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2017Article
- Similarity-based modeling applied to signal detection in pharmacovigilance.CPT: pharmacometrics & systems pharmacology · 2014Article
- Bridging islands of information to establish an integrated knowledge base of drugs and health outcomes of interest.Drug safety · 2014Article
- Desideratum for evidence based epidemiology.Drug safety · 2013Review
- Replication of the OMOP experiment in Europe: evaluating methods for risk identification in electronic health record databases.Drug safety · 2013Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe availability of large-scale observational healthcare data allows for the active monitoring of safety of drugs, but research is needed to determine which statistical methods are best suited for this task. Recently, the Longitudinal Gamma Poisson Shrinker (LGPS) and Longitudinal Evaluation of Observational Profiles of Adverse events Related to Drugs (LEOPARD) methods were developed specifically for this task. LGPS applies Bayesian shrinkage to an estimated incidence rate ratio, and LEOPARD aims to detect and discard associations due to protopathic bias. The operating characteristics of these methods still need to be determined.
objectiveEstablish the operating characteristics of LGPS and LEOPARD for large scale observational analysis in drug safety. RESEARCH
designWe empirically evaluated LGPS and LEOPARD in five real observational healthcare databases and six simulated datasets. We retrospectively studied the predictive accuracy of the methods when applied to a collection of 165 positive control and 234 negative control drug-outcome pairs across four outcomes: acute liver injury, acute myocardial infarction, acute kidney injury, and upper gastrointestinal bleeding.
resultsIn contrast to earlier findings, we found that LGPS and LEOPARD provide weak discrimination between positive and negative controls, although the use of LEOPARD does lead to higher performance in this respect. Furthermore, the methods produce biased estimates and confidence intervals that have poor coverage properties.
conclusionsFor the four outcomes we examined, LGPS and LEOPARD may not be the designs of choice for risk identification.
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