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The Development and Validation of a Decision-Analytic Model Representing the Full Disease Course of Acute Myeloid Leukemia

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Abstract

Background

The treatment of acute myeloid leukemia (AML) is moving towards personalized medicine. However, due to the low incidence of AML, it is not always feasible to evaluate the cost-effectiveness of personalized medicine using clinical trials. Decision analytic models provide an alternative data source.

Objective

The aim of this study was to develop and validate a decision analytic model that represents the full disease course of AML.

Methods

We used a micro simulation with discrete event components to incorporate both patient and disease heterogeneity. Input parameters were calculated from patient-level data. Two hematologists critically evaluated the model to ensure face validity. Internal and external validity was tested by comparing complete remission (CR) rates and survival outcomes of the model with original data, other clinical trials and a population-based study.

Results

No significant differences in patient and treatment characteristics, CR rate, 5-year overall and disease-free survival were found between the simulated and original data. External validation showed no significant differences in survival between simulated data and other clinical trials. However, differences existed between the simulated data and a population-based study.

Conclusions

The model developed in this study is proved to be valid for analysis of an AML population participating in a clinical trial. The generalizability of the model to a broader patient population has not been proven yet. Further research is needed to identify differences between the clinical trial population and other AML patients and to incorporate these differences in the model.

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Notes

  1. Lognormal distribution: log(survival time ) is normally distributed with mean = β 0 + β 1 X 1 + … + β n X n , and standard deviation = shape parameter

    Loglogistic distribution: survival time = scale × (p/(1 − p))(1/shape), where scale = exp(β 0 + β 1 X 1 + … + β n X n ).

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Acknowledgments

Annemieke Leunis is the guarantor for the overall content in this article. She has performed the literature review, designed the study, performed the analyses, developed the model and wrote the article. Ken Redekop and Carin Uyl-de Groot gave advice in the design of the study, the analysis and the development of the model. Kees van Montfoort gave advice regarding the statistical methods used and provided the original patient-level data. Bob Löwenberg was the principal investigator in the studies of which patient-level data was collected. Furthermore, he gave clinical advice regarding the model. All authors reviewed draft versions of the paper and gave permission for the final version of the paper to be published. This work was supported by a grant from the Center for Translational Molecular Medicine (CTMM), project BioCHIP (grant 03O-102). CTMM did not have any influence on the design, analysis and reporting of the study. The authors want to thank Professor Pieter Sonneveld for his expert advice in the validation of the decision model and Wim van Putten from the Hovon Data Center for providing access to patient level data and his statistical advice during the development of the model. None of the authors have any conflict of interest to declare.

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Authors and Affiliations

  1. Institute for Medical Technology Assessment/Institute of Health Policy and Management, Erasmus University Rotterdam, PO Box 1738, 3000 DR, Rotterdam, The Netherlands

    Annemieke Leunis, W. Ken Redekop & Carin A. Uyl-de Groot

  2. Department of Statistics, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands

    Kees A. G. M. van Montfort

  3. Department of Hematology, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands

    Bob Löwenberg

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  1. Annemieke Leunis
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Correspondence to Annemieke Leunis.

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Leunis, A., Redekop, W.K., van Montfort, K.A.G.M. et al. The Development and Validation of a Decision-Analytic Model Representing the Full Disease Course of Acute Myeloid Leukemia. PharmacoEconomics 31, 605–621 (2013). https://doi.org/10.1007/s40273-013-0058-1

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