Integrative Methods for Improved Pharmacovigilance

改善药物警戒的综合方法

• 批准号: 8055383
• 负责人: Ben Y Reis
• 金额: $ 24.49万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-01 至 2013-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8055383
• 关键词: Address; Adverse drug effect; Adverse effects; Adverse event; Boxing; Canada; Cessation of life; Data; Data Reporting; Data Sources; Databases; Detection; Disease; Drug usage; Early Diagnosis; Environment; Epidemiology; Event; Goals; Health; Healthcare; Insurance Carriers; Investigation; Knowledge; Methods; Monitor; Morbidity - disease rate; National Institute of General Medical Sciences; Performance; Pharmaceutical Preparations; Phase; Public Health; Relative (related person); Reporting; Research; Risk Assessment; Rofecoxib; Safety; Sales; Sampling; Scientist; Selection Criteria; Sensitivity and Specificity; Sentinel; Signal Transduction; Source; Specialist; Staging; Strategic Planning; Systems Analysis; Techniques; Testing; Time; Withdrawal; Work; base; cerivastatin; computer based statistical methods; design; experience; improved; innovation; mortality; network models; novel; post-market; prevent; prospective; public health relevance; tool; vigilance

DESCRIPTION (provided by applicant): Early detection of adverse drug events in the post-market phase is essential for protecting the public from significant morbidity and mortality. The broad, long-term objectives of this project are to develop tools and techniques that enable scientists to discover adverse drug events earlier and more reliably. Current drug safety approaches rely on analyses of either spontaneous reports or healthcare claims, and scientists are over-whelmed by the large amounts of disparate drug safety information. Integration is urgently needed to combine these complementary perspectives to improve adverse event discovery. The goal of this project is to develop integrated pharmacovigilance methods that combine information across multiple drugs and data sources to provide a more comprehensive view of drug safety. Distributed integration methods will allow organizations to collaborate on pharmacovigilance without exchanging private health data. Methods will be evaluated using fifty drug use cases, US and Canadian spontaneous report data, and claims data from the US's largest insurer: I. Develop multivariate network approaches to improve adverse event discovery using claims data. Current claims-based methods rely on a single pharmacoepidemiological comparison between two drugs. A pharmacoepidemiological network approach will be developed that combines multiple drug-drug comparisons to produce a unified picture of the drug safety environment, employing a sequential analysis approach to address multiple-testing over time. Detection performance will be evaluated, and will be compared to the standard single-reference-drug approach. The effects of network size and composition will also be studied. II. Integrate multiple data sources to improve adverse event discovery using spontaneous reports. Traditional disproportionality-based signal detection methods, including PRR and RRR, will be applied to the US AERS and Canada Vigilance databases. The effects of reporting volume on signal detectability will be studied using sub-sampling. Aggregative and Bayesian multi-univariate approaches will be developed to integrate the US and Canadian data, and their performance will be compared to single-data-source approaches. Spontaneous report-based methods will be compared to claims-based methods in order to investigate their relative strengths and weaknesses and characterize their temporal interrelationships. III. Develop distributed discovery methods that integrate spontaneous reports with claims data. Three distributed approaches for integrating spontaneous reports and claims data will be developed to allow scientists to collaborate on pharmacovigilance across organizations without exchanging private health data: A) Extending spontaneous-report-based signal detection methods to incorporate the findings from claims data. B) Extending claims-based signal detection methods to incorporate the findings from spontaneous reports. C) Developing dynamic Bayesian network models that exploit the temporal relationships between sources. The performance of these integration approaches will be compared to single-data-source approaches. PUBLIC HEALTH RELEVANCE: Some drugs that are approved for sale to the public may have dangerous unknown side-effects. It is important to detect these unknown side effects as soon as possible in order to prevent serious illness or death. This project will help protect the public health by improving the ability to detect unknown dangerous drug side effects earlier and more reliably.

描述（由申请人提供）：在市场后阶段对不良药物事件的早期检测对于保护公众免受明显的发病率和死亡率至关重要。该项目的广泛，长期目标是开发工具和技术，使科学家能够更早，更可靠地发现不良药物事件。当前的药物安全方法依赖于自发报告或医疗保健主张的分析，科学家对大量不同的药物安全信息所折磨。迫切需要集成以结合这些互补观点以改善不良事件发现。该项目的目的是开发综合的药物宣传方法，该方法结合了多种药物和数据源的信息，以提供对药物安全的更全面的看法。分布式集成方法将使组织能够在不交换私人健康数据的情况下进行药物宣传协作。方法将使用五十种药物用例，美国和加拿大自发报告数据进行评估，并索取美国最大保险公司的数据： I.开发多元网络方法，使用索赔数据改善不良事件发现。当前基于索赔的方法依赖于两种药物之间的单一药物ePIDEPIDEMIological比较。将开发出一种药物电子网络网络方法，该方法结合了多种药物比较，以产生药物安全环境的统一图片，采用顺序分析方法来解决多次测试。将评估检测性能，并将其与标准的单参考 - 药物方法进行比较。还将研究网络大小和组成的影响。 ii。集成多个数据源，以使用自发报告改善不良事件发现。包括PRR和RRR在内的传统基于不成比例的信号检测方法将应用于美国Aers和Canada Vigilance数据库。报告体积对信号可检测性的影响将使用子采样研究。将开发汇总和贝叶斯多单变量方法来整合美国和加拿大的数据，并将其性能与单DATA源方法进行比较。将自发报告的方法与基于索赔的方法进行比较，以研究其相对优势和劣势并表征其时间相互关系。 iii。开发分布式发现方法将自发报告与索赔数据集成在一起。将开发三种用于整合自发报告和索赔数据的分布式方法，以允许科学家在无需交换私人健康数据的情况下就整个组织的药物宣传协作： a）扩展基于自发报告的信号检测方法，以合并索赔数据中的发现。 b）扩展基于索赔的信号检测方法，以合并自发报告中的发现。 c）开发动态的贝叶斯网络模型，以利用来源之间的时间关系。这些集成方法的性能将与单DATA源方法进行比较。 公共卫生相关性：一些被批准出售给公众的药物可能具有危险的未知副作用。重要的是要尽快检测这些未知的副作用，以防止严重疾病或死亡。该项目将通过提高更早，更可靠地检测未知危险药物副作用的能力来帮助保护公共卫生。