Integrative Methods for Improved Pharmacovigilance

改善药物警戒的综合方法

• 批准号: 8232024
• 负责人: Ben Y Reis
• 金额: $ 21.09万
• 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 2015-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8232024
• 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（由申请方提供）：在上市后阶段早期发现药物不良事件对于保护公众免受重大发病率和死亡率的影响至关重要。该项目的长期目标是开发工具和技术，使科学家能够更早、更可靠地发现药物不良事件。目前的药物安全方法依赖于对自发报告或医疗保健索赔的分析，科学家们被大量不同的药物安全信息所淹没。迫切需要整合联合收割机来结合这些互补的观点，以改善不良事件的发现。该项目的目标是开发综合药物警戒方法，将多种药物和数据源的信息联合收割机结合起来，以提供更全面的药物安全性视图。分布式集成方法将允许组织在药物警戒方面进行协作，而无需交换私人健康数据。将使用50个药物使用案例、美国和加拿大自发报告数据以及美国最大保险公司的索赔数据来评估方法： I.开发多变量网络方法，利用索赔数据改进不良事件发现。目前基于声明的方法依赖于两种药物之间的单一药物流行病学比较。将开发一种药物流行病学网络方法，该方法结合多种药物之间的比较，以产生药物安全性环境的统一图像，采用序贯分析方法来解决随时间推移的多重检测。将评价检测性能，并与标准单一参比药物方法进行比较。还将研究网络大小和组成的影响。 二.整合多个数据源，通过自发报告改善不良事件发现。传统的基于概率的信号检测方法，包括PRR和RRR，将应用于美国AERS和加拿大Vigilance数据库。将使用二次采样研究报告量对信号可检测性的影响。将开发综合和贝叶斯多单变量方法，以整合美国和加拿大的数据，并将其性能与单一数据源方法进行比较。将对基于自发报告的方法和基于索赔的方法进行比较，以研究它们的相对优势和劣势，并说明它们在时间上的相互关系。 三.开发分布式发现方法，将自发报告与索赔数据集成。将开发三种分布式方法来整合自发报告和索赔数据，以使科学家能够在不交换私人健康数据的情况下跨组织开展药物警戒合作： A）扩展基于自发报告的信号检测方法，以纳入索赔数据的发现。 B）扩展基于声明的信号检测方法，以纳入自发报告的结果。 C）开发利用源之间的时间关系的动态贝叶斯网络模型。这些集成方法的性能将与单一数据源方法进行比较。 公共卫生相关性：一些批准向公众销售的药物可能具有危险的未知副作用。重要的是要尽快发现这些未知的副作用，以防止严重的疾病或死亡。该项目将通过提高更早和更可靠地检测未知危险药物副作用的能力，帮助保护公众健康。