title: Global registries for measuring pharmacoeconomic and quality-of-life outcomes: focus on design and data collection, analysis and interpretation.
abstract: Disease registries have traditionally been vehicles for the collection of clinical data, in most instances following a large number of patients for a long time period in an observational manner, and enhancing our understanding of disease aetiology and epidemiology. However, over recent decades, the potential for additional data collection and analyses to be conducted within the framework of a registry has been recognised and utilised. This is evident by the sheer number of registries that are now referenced in the medical literature, covering a vast array of therapeutic areas and topics much more varied than incidence, prevalence and survival. The opportunity to collect QOL and pharmacoeconomic data has been utilised within the registry framework as more and more countries have increased their demands for such information for regulatory procedures, including pricing and reimbursement decisions. This increased need for information has led to a marked increase in the number of registries undertaken that are primarily sponsored by the pharmaceutical industry. Disease registries offer tremendous opportunities to realise improvements in care. The length of data collection and the large number of patients involved offer some unusual advantages for QOL and health economic analyses; however, these advantages are not yet fully exploited.
authors.affiliation: Quintiles Limited, Market Street, Bracknell RG12 1HX, UK.
authors.forename: Lisa
authors.initials: L
authors.lastname: Kennedy
authors.forename: Ann-Marie
authors.initials: AM
authors.lastname: Craig
mesh_terms: D003625:Data Collection / Q000379:methods; D003627:Data Interpretation, Statistical; D004358:Drug Therapy / Q000191:economics*; D014943:Global Health; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000191:economics*; D011788:Quality of Life; D012042:Registries / Q000592:standards* / Q000706:statistics & numerical data; D012107:Research Design