PubMed · 14997123
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
William E Ott. 2004. Software evaluation.. https://pubmed.ncbi.nlm.nih.gov/14997123/
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
Explore the source record for details and available documents.
Policy analysis is not an easy task. Its scope is broad and can include both the analysis of policy process and the analysis of policy content. This paper is concerned with the analysis of policy content and offers some practical guidance regarding how to analyse health policy and link it to health outcomes. An eight-step framework for policy analysis is proposed that public health policy makers and public health practitioners may find especially useful due to its simplicity.
Pharmacoeconomics is vitally important to drug manufacturers in terms of communicating to external decision-makers (payers, prescribers, patients) the value of their products, achieving regulatory and reimbursement approval and contributing to commercial success. Since development of new drugs is long, costly and risky, and decisions must be made how to allocate considerable research and development (R&D) resources, pharmacoeconomics also has an essential role informing internal decision-making (within a company) during drug development. The use of pharmacoeconomics in early development phases is likely to enhance the efficiency of R&D resource use and also provide a solid foundation for communicating product value to external decision-makers further downstream, increasing the likelihood of regulatory (reimbursement) approval and commercial success. This paper puts the case for use of pharmacoeconomic analyses earlier in the development process and outlines five techniques (clinical trial simulation [CTS], option pricing [OP], investment appraisal [IA], threshold analysis [TA] and value of information [VOI] analysis) that can provide useful input into the design of clinical development programmes, portfolio management and optimal pricing strategy. CTS can estimate efficacy and tolerability profiles before clinical data are available. OP can show the value of different clinical programme designs, sequencing of studies and stop decisions. IA can compare expected net present value (NPV) of different product profiles or study designs. TA can be used to understand development drug profile requirements given partial data. VOI can assist risk management by quantifying uncertainty and assessing the economic viability of gathering further information on the development drug. No amount of pharmacoeconomic data can make a bad drug good; what it can do is enhance the drug developers understanding of the characteristics of that drug. Decision-making, in light of this information, is likely to be better than that without it, whether it leads to faster termination of uneconomic projects or the allocation of more appropriate resources to attractive projects.