Literature DB >> 18058847

Cost-effectiveness analysis in colorectal cancer using a semi-Markov model.

Christel Castelli1, Christophe Combescure, Yohann Foucher, Jean-Pierre Daures.   

Abstract

Cost and effectiveness are usually modeled according to one studied event or one health state with parametric or non-parametric methods. In this paper, we propose an original method for assessing total costs while incorporating the dynamics of change in the health status of patients. A semi-Markov model in which the distributions of sojourn times are explicitly defined is developed. The hazard function of sojourn times is modeled by Weibull distributions specific to each transition. A vector of covariates is incorporated into the hazard function of each transition. From a regression model for costs, a cumulative cost function is derived. An estimation of the mean cost per patient in each state defined in the semi-Markov model could thus be made, and this enables us to identify the determinants of direct costs. The results of incremental net benefit (INB) are assessed using the bootstrap method. A cost-effectiveness analysis is performed in order to compare two strategies of follow-up in the colorectal cancer study. Two hundred and forty patients were enrolled in this study. Three health states are defined for patients with curative resection of colorectal cancer: alive without relapse, alive with relapse, and dead. The mean survival is 4.35 and 4.12 years, respectively, in the standard and moderate follow-up groups. We show that mean cost differs significantly by follow-up strategy and Dukes stage. Finally, the INB is assessed and this indicates that neither of the strategies compared was more cost-effective than the other. Copyright (c) 2007 John Wiley & Sons, Ltd.

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Year:  2007        PMID: 18058847     DOI: 10.1002/sim.3112

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  4 in total

Review 1.  Overview of parametric survival analysis for health-economic applications.

Authors:  K Jack Ishak; Noemi Kreif; Agnes Benedict; Noemi Muszbek
Journal:  Pharmacoeconomics       Date:  2013-08       Impact factor: 4.981

2.  Are Markov and semi-Markov models flexible enough for cognitive panel data?

Authors:  Richard J Kryscio; Erin L Abner
Journal:  J Biom Biostat       Date:  2013-01-01

3.  Adjusting for mortality when identifying risk factors for transitions to mild cognitive impairment and dementia.

Authors:  Richard J Kryscio; Erin L Abner; Yushun Lin; Gregory E Cooper; David W Fardo; Gregory A Jicha; Peter T Nelson; Charles D Smith; Linda J Van Eldik; Lijie Wan; Frederick A Schmitt
Journal:  J Alzheimers Dis       Date:  2013       Impact factor: 4.160

4.  On the Use of Markov Models in Pharmacoeconomics: Pros and Cons and Implications for Policy Makers.

Authors:  Andrea Carta; Claudio Conversano
Journal:  Front Public Health       Date:  2020-10-30
  4 in total

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