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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
| Content Provider | SAGE Publishing |
|---|---|
| Author | Williams, Claire Lewsey, James D. Mackay, Daniel F. Briggs, Andrew H. |
| Copyright Year | 2016 |
| 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. |
| Related Links | https://journals.sagepub.com/doi/pdf/10.1177/0272989X16670617?download=true |
| Starting Page | 427 |
| Ending Page | 439 |
| Page Count | 13 |
| ISSN | 0272989X |
| Issue Number | 4 |
| Volume Number | 37 |
| Journal | Medical Decision Making (MDM) |
| e-ISSN | 1552681X |
| DOI | 10.1177/0272989X16670617 |
| Language | English |
| Publisher | Sage Publications CA |
| Publisher Date | 2016-10-03 |
| Publisher Place | Los Angeles |
| Access Restriction | Open |
| Rights Holder | © The Author(s) 2016 |
| Subject Keyword | survival analysis Markov models cost-effectiveness analysis oncology |
| Content Type | Text |
| Resource Type | Article |
| Subject | Health Policy |
