OBJECTIVES: To derive robust estimates for the cost of multiple sclerosis (MS) based on a variety of cost factors across a number of different cost perspectives. This is essential to perform credible pharmacoeconomic evaluations of alternative MS therapies. METHODS: Here we present a detailed analysis of previously published MS cost data for the UK to which we fit a seemingly unrelated regression. This allows us to assess the size and significance of different cost factors, and account for the covariance between cost perspectives. RESULTS: We show that disability severity, disease type, relapse status, treatment type and time of treatment, sex, age, educational status, and time since diagnosis, are significant cost factors, with the significance of each dependent on the cost perspective chosen. CONCLUSIONS: This analysis provides a statistical model that may be used to better estimate individual patient costs across a range of demographic and cost perspectives, for use by health planners and in pharmacoeconomic evaluations.
OBJECTIVES: To derive robust estimates for the cost of multiple sclerosis (MS) based on a variety of cost factors across a number of different cost perspectives. This is essential to perform credible pharmacoeconomic evaluations of alternative MS therapies. METHODS: Here we present a detailed analysis of previously published MS cost data for the UK to which we fit a seemingly unrelated regression. This allows us to assess the size and significance of different cost factors, and account for the covariance between cost perspectives. RESULTS: We show that disability severity, disease type, relapse status, treatment type and time of treatment, sex, age, educational status, and time since diagnosis, are significant cost factors, with the significance of each dependent on the cost perspective chosen. CONCLUSIONS: This analysis provides a statistical model that may be used to better estimate individual patient costs across a range of demographic and cost perspectives, for use by health planners and in pharmacoeconomic evaluations.
Authors: Bianca Weinstock-Guttman; Steven L Galetta; Gavin Giovannoni; Eva Havrdova; Michael Hutchinson; Ludwig Kappos; Paul W O'Connor; J Theodore Phillips; Chris Polman; William H Stuart; Frances Lynn; Christophe Hotermans Journal: J Neurol Date: 2011-10-19 Impact factor: 4.849
Authors: Jasmina I Ivanova; Howard G Birnbaum; Seth Samuels; Matthew Davis; Amy L Phillips; Dennis Meletiche Journal: Pharmacoeconomics Date: 2009 Impact factor: 4.981
Authors: Ray Gani; Gavin Giovannoni; David Bates; Belinda Kemball; Steve Hughes; John Kerrigan Journal: Pharmacoeconomics Date: 2008 Impact factor: 4.981
Authors: Crystal Watson; Christine Prosser; Sebastian Braun; Pamela B Landsman-Blumberg; Erika Gleissner; Sarah Naoshy Journal: Clinicoecon Outcomes Res Date: 2017-02-01
Authors: Wayne Smith; Paul McCrone; Cassie Goddard; Wei Gao; Rachel Burman; Diana Jackson; Irene Higginson; Eli Silber; Jonathan Koffman Journal: Mult Scler Int Date: 2014-02-05