Anthony J Hatswell1, Joshua Porter2, Dawn Lee2, Nadine Hertel3, Nicholas R Latimer4. 1. BresMed, Sheffield, South Yorkshire, UK; University College London, London, UK. Electronic address: ahatswell@bresmed.co.uk. 2. BresMed, Sheffield, South Yorkshire, UK. 3. Bristol-Myers Squibb Pharmaceuticals, Ltd., Uxbridge, Middlesex, UK. 4. School of Health and Related Research, University of Sheffield, Sheffield, South Yorkshire, UK.
Abstract
BACKGROUND: The cost of pharmaceuticals dosed by weight or body surface area (BSA) can be estimated in several ways for economic evaluations. A review of 20 recent National Institute for Health and Care Excellence appraisals showed that 17 of them took the mean weight or BSA of patients, 2 costed the individual patient data from trials, and 2 fitted a distribution to patient-level data. OBJECTIVES: To investigate the estimated drug costs using different methodologies to account for patient characteristics for pharmaceuticals with a weight- or BSA-based posology. The secondary objective was to explore the suitability of general population data as a proxy for patient-level data. METHODS: Patient-level data were pooled from three clinical trials and used to calculate a hypothetical cost per administration of eight licensed pharmaceuticals, applying the three methods used in recent National Institute for Health and Care Excellence appraisals. The same analysis was performed using data from the Health Survey for England (in place of patient-level data) to investigate the validity of using general population data as a substitute for patient-level data. RESULTS: Compared with using patient-level data from clinical trials, the mean patient characteristics (weight or BSA) led to an underestimation of drug cost by 6.1% (range +1.5% to -25.5%). Fitting a distribution to patient-level data led to a mean difference of +0.04%. All estimates were consistent using general population data. CONCLUSIONS: Estimation of drug costs in health economic evaluation should account for the distribution in weight or BSA to produce accurate results. When patient data are not available, general population data may be used as an alternative.
BACKGROUND: The cost of pharmaceuticals dosed by weight or body surface area (BSA) can be estimated in several ways for economic evaluations. A review of 20 recent National Institute for Health and Care Excellence appraisals showed that 17 of them took the mean weight or BSA of patients, 2 costed the individual patient data from trials, and 2 fitted a distribution to patient-level data. OBJECTIVES: To investigate the estimated drug costs using different methodologies to account for patient characteristics for pharmaceuticals with a weight- or BSA-based posology. The secondary objective was to explore the suitability of general population data as a proxy for patient-level data. METHODS:Patient-level data were pooled from three clinical trials and used to calculate a hypothetical cost per administration of eight licensed pharmaceuticals, applying the three methods used in recent National Institute for Health and Care Excellence appraisals. The same analysis was performed using data from the Health Survey for England (in place of patient-level data) to investigate the validity of using general population data as a substitute for patient-level data. RESULTS: Compared with using patient-level data from clinical trials, the mean patient characteristics (weight or BSA) led to an underestimation of drug cost by 6.1% (range +1.5% to -25.5%). Fitting a distribution to patient-level data led to a mean difference of +0.04%. All estimates were consistent using general population data. CONCLUSIONS: Estimation of drug costs in health economic evaluation should account for the distribution in weight or BSA to produce accurate results. When patient data are not available, general population data may be used as an alternative.
Authors: D Lee; A Amadi; J Sabater; J Ellis; H Johnson; S Kotapati; S McNamara; A Walker; M Cooper; K Patterson; N Roskell; Y Meng Journal: Pharmacoecon Open Date: 2019-03
Authors: Carla Mamolo; Verna Welch; Roland B Walter; Joseph C Cappelleri; James Brockbank; Matthew Cawson; Chris Knight; Michele Wilson Journal: Pharmacoeconomics Date: 2020-11-25 Impact factor: 4.981