Martin Hoyle1. 1. Peninsula Technology Assessment Group (PenTAG), Peninsular College of Medicine and Dentistry, University of Exeter, Exeter, UK. martin.hoyle@pms.ac.uk
Abstract
OBJECTIVES: The expected lifetime of a health technology is a critical parameter in value of information analysis and in two methodologies for cost-effectiveness analysis which have recently been suggested. The first method allows for the possibility that a superior technology will become available in the future. The second advocates modeling both the prevalent and all future incident patient cohorts. Unfortunately, for value of information analysis, the period of time over which information about the decision problem would be useful is very uncertain, and existing estimates are seemingly arbitrary. Furthermore, there is very little literature on the historical lifetimes of technologies. Here, I quantify and analyze the historical lifetimes of drugs in England. I then apply this information to inform the value of further research and the cost-effectiveness of health technologies. METHODS: A Weibull regression model was fitted to the historical drug lifetimes of 455 drugs. These represented all British National Formulary drugs in England which were launched from 1981 to 2007, and which did not have very low sales volumes. RESULTS: The mean drug lifetime was 57 years (95% confidence interval 39-79 years), and the median was 46 years (35-60 years). Drugs with low sales volumes tended to have shorter lifetimes. Under certain assumptions, the ratio of population level to per-year expected value of information is 21. Drug lifetimes are used to parameterize the two models of cost-effectiveness. CONCLUSIONS: The distribution function of the historical lifetimes of drugs can inform suitable time horizons for: 1) value of information; and 2) cost-effectiveness analyses related to drugs.
OBJECTIVES: The expected lifetime of a health technology is a critical parameter in value of information analysis and in two methodologies for cost-effectiveness analysis which have recently been suggested. The first method allows for the possibility that a superior technology will become available in the future. The second advocates modeling both the prevalent and all future incident patient cohorts. Unfortunately, for value of information analysis, the period of time over which information about the decision problem would be useful is very uncertain, and existing estimates are seemingly arbitrary. Furthermore, there is very little literature on the historical lifetimes of technologies. Here, I quantify and analyze the historical lifetimes of drugs in England. I then apply this information to inform the value of further research and the cost-effectiveness of health technologies. METHODS: A Weibull regression model was fitted to the historical drug lifetimes of 455 drugs. These represented all British National Formulary drugs in England which were launched from 1981 to 2007, and which did not have very low sales volumes. RESULTS: The mean drug lifetime was 57 years (95% confidence interval 39-79 years), and the median was 46 years (35-60 years). Drugs with low sales volumes tended to have shorter lifetimes. Under certain assumptions, the ratio of population level to per-year expected value of information is 21. Drug lifetimes are used to parameterize the two models of cost-effectiveness. CONCLUSIONS: The distribution function of the historical lifetimes of drugs can inform suitable time horizons for: 1) value of information; and 2) cost-effectiveness analyses related to drugs.