title: Estimation of Survival Probabilities for Use in Cost-effectiveness Analyses: A Comparison of a Multi-state Modeling Survival Analysis Approach with Partitioned Survival and Markov Decision-Analytic Modeling.
abstract: Modeling of clinical-effectiveness in a cost-effectiveness analysis typically involves some form of partitioned survival or Markov decision-analytic modeling. The health states progression-free, progression and death and the transitions between them are frequently of interest. With partitioned survival, progression is not modeled directly as a state; instead, time in that state is derived from the difference in area between the overall survival and the progression-free survival curves. With Markov decision-analytic modeling, a priori assumptions are often made with regard to the transitions rather than using the individual patient data directly to model them. This article compares a multi-state modeling survival regression approach to these two common methods. As a case study, we use a trial comparing rituximab in combination with fludarabine and cyclophosphamide v. fludarabine and cyclophosphamide alone for the first-line treatment of chronic lymphocytic leukemia. We calculated mean Life Years and QALYs that involved extrapolation of survival outcomes in the trial. We adapted an existing multi-state modeling approach to incorporate parametric distributions for transition hazards, to allow extrapolation. The comparison showed that, due to the different assumptions used in the different approaches, a discrepancy in results was evident. The partitioned survival and Markov decision-analytic modeling deemed the treatment cost-effective with ICERs of just over £16,000 and £13,000, respectively. However, the results with the multi-state modeling were less conclusive, with an ICER of just over £29,000. This work has illustrated that it is imperative to check whether assumptions are realistic, as different model choices can influence clinical and cost-effectiveness results.
authors.affiliation: Health Economics and Health Technology Assessment, Institute of Health and Wellbeing, University of Glasgow, Glasgow (CW, JDL, AHB).
authors.forename: Claire
authors.initials: C
authors.lastname: Williams
authors.affiliation: Health Economics and Health Technology Assessment, Institute of Health and Wellbeing, University of Glasgow, Glasgow (CW, JDL, AHB).
authors.forename: James D
authors.initials: JD
authors.lastname: Lewsey
authors.affiliation: Public Health, Institute of Health and Wellbeing, University of Glasgow, Glasgow (DFM).
authors.forename: Daniel F
authors.initials: DF
authors.lastname: Mackay
authors.affiliation: Health Economics and Health Technology Assessment, Institute of Health and Wellbeing, University of Glasgow, Glasgow (CW, JDL, AHB).
authors.forename: Andrew H
authors.initials: AH
authors.lastname: Briggs
mesh_terms: D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use; D003362:Cost-Benefit Analysis / Q000379:methods*; D003627:Data Interpretation, Statistical; D003661:Decision Support Techniques; D018572:Disease-Free Survival; D006801:Humans; D015451:Leukemia, Lymphocytic, Chronic, B-Cell / Q000188:drug therapy / Q000401:mortality; D008390:Markov Chains; D011336:Probability; D019057:Quality-Adjusted Life Years; D016019:Survival Analysis
keywords: Markov models; cost-effectiveness analysis; oncology; survival analysis