Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention
Abstract Markov chain models are commonly used to simulate the natural history of human papillomavirus infection and subsequent cervical lesions with the aim of predicting future benefits of health interventions. Developing and calibrating these models entails making a number of critical decisions t...
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doaj-8e10defb16fb4e749525b5a5bde2969f2020-12-08T02:40:22ZengNature Publishing GroupScientific Reports2045-23222017-12-01711810.1038/s41598-017-17215-2Impact of model calibration on cost-effectiveness analysis of cervical cancer preventionDavid Moriña0Silvia de Sanjosé1Mireia Diaz2Unit of Infections and Cancer - Information and Interventions (UNIC - I&I), Cancer Epidemiology Research Program (CERP), Catalan Institute of Oncology (ICO)-IDIBELL, L’Hospitalet de LlobregatCancer Epidemiology Research Program (CERP), Catalan Institute of Oncology (ICO)-IDIBELL, L’Hospitalet de LlobregatUnit of Infections and Cancer - Information and Interventions (UNIC - I&I), Cancer Epidemiology Research Program (CERP), Catalan Institute of Oncology (ICO)-IDIBELL, L’Hospitalet de LlobregatAbstract Markov chain models are commonly used to simulate the natural history of human papillomavirus infection and subsequent cervical lesions with the aim of predicting future benefits of health interventions. Developing and calibrating these models entails making a number of critical decisions that will influence the ability of the model to reflect real conditions and predict future situations. Accuracy of selected inputs and calibration procedures are two of the crucial aspects for model performance and understanding their influence is essential, especially when involves policy decisions. The aim of this work is to assess the health and economic impact on cervical cancer prevention strategies currently under discussion according to the most common methods of model calibration combined with different accuracy degree of initial inputs. Model results show large differences on the goodness of fit and cost-effectiveness outcomes depending on the calibration approach used, and these variations may affect health policy decisions. Our findings strengthen the importance of obtaining good calibrated probability matrices to get reliable health and cost outcomes, and are directly generalizable to any cost-effectiveness analysis based on Markov chain models.https://doi.org/10.1038/s41598-017-17215-2 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
David Moriña Silvia de Sanjosé Mireia Diaz |
spellingShingle |
David Moriña Silvia de Sanjosé Mireia Diaz Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention Scientific Reports |
author_facet |
David Moriña Silvia de Sanjosé Mireia Diaz |
author_sort |
David Moriña |
title |
Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention |
title_short |
Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention |
title_full |
Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention |
title_fullStr |
Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention |
title_full_unstemmed |
Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention |
title_sort |
impact of model calibration on cost-effectiveness analysis of cervical cancer prevention |
publisher |
Nature Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2017-12-01 |
description |
Abstract Markov chain models are commonly used to simulate the natural history of human papillomavirus infection and subsequent cervical lesions with the aim of predicting future benefits of health interventions. Developing and calibrating these models entails making a number of critical decisions that will influence the ability of the model to reflect real conditions and predict future situations. Accuracy of selected inputs and calibration procedures are two of the crucial aspects for model performance and understanding their influence is essential, especially when involves policy decisions. The aim of this work is to assess the health and economic impact on cervical cancer prevention strategies currently under discussion according to the most common methods of model calibration combined with different accuracy degree of initial inputs. Model results show large differences on the goodness of fit and cost-effectiveness outcomes depending on the calibration approach used, and these variations may affect health policy decisions. Our findings strengthen the importance of obtaining good calibrated probability matrices to get reliable health and cost outcomes, and are directly generalizable to any cost-effectiveness analysis based on Markov chain models. |
url |
https://doi.org/10.1038/s41598-017-17215-2 |
work_keys_str_mv |
AT davidmorina impactofmodelcalibrationoncosteffectivenessanalysisofcervicalcancerprevention AT silviadesanjose impactofmodelcalibrationoncosteffectivenessanalysisofcervicalcancerprevention AT mireiadiaz impactofmodelcalibrationoncosteffectivenessanalysisofcervicalcancerprevention |
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1724393494690136064 |