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Evaluating Disease-Modifying Agents: A Simulation Framework for Alzheimer’s Disease

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Abstract

Background

Considerable advances have been made in modeling Alzheimer’s disease (AD), with a move towards individual-level rather than cohort models and simulations that consider multiple dimensions when evaluating disease severity. However, the possibility that disease-modifying agents (DMAs) may emerge requires an update of existing modeling frameworks.

Objectives

The aim of this study was to develop a simulation allowing for economic evaluation of DMAs in AD.

Methods

The model was developed based on a previously published, well-validated, discrete event simulation which measures disease severity on the basis of cognition, behaviour, and function, and captures the interrelated changes in these measures for individuals. The updated model adds one more domain, patient dependence, in addition to cognition, behaviour, and function to better characterize disease severity. Furthermore, the model was modified to have greater flexibility in assessing the impact of various important assumptions, such as the long-term effectiveness of DMAs and their impact on survival, on model outcomes. A validation analysis was performed to examine how well the model predicted change in disease severity among patients not receiving DMA treatment by comparing model results to those observed in two recent phase III clinical trials of bapineuzumab. In addition, various hypothetical scenarios were tested to demonstrate the improved features of the model.

Results

Validation results show that the model closely predicts the mean changes in disease severity over 18 months. Results from different hypothetical scenarios show that the model allows for credible assessment of those major uncertainties surrounding the long-term effectiveness of DMAs, including the potential impact of improved survival with DMA treatment. They also indicate that varying these assumptions could have a major impact on the value of DMAs.

Conclusions

The updated economic model has good predictive power, but validation against longer-term outcomes is still needed. Our analyses also demonstrate the importance of designing a model with sufficient flexibility such that the model allows for assessment of the impact of key sources of uncertainty on the value of DMAs.

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Acknowledgments

The authors would like to thank Drs. Peter Neumann and Linus Jönsson for their helpful feedback during the development of the model and the manuscript.

Funding

This study was sponsored by Pfizer Inc. and Janssen Alzheimer Immunotherapy Research & Development, LLC.

Role of the funding source

Gwilym Thompson and Maren Gaudig are employees of Janssen Alzheimer Immunotherapy Research & Development, LLC. Loretto Lacey was an employee of Janssen Alzheimer Immunotherapy Research & Development, LLC, at the time this research was conducted. Joel Bobula is an employee of Pfizer Inc. All were involved in the design and conduct of the study.

Conflicts of interest

Denis Getsios and Shien Guo are employees of Evidera, who were paid consultants to Pfizer Inc. and Janssen Alzheimer Immunotherapy Research and Development, LLC, in connection with the development of this manuscript. Nikhil Revankar and Peng Xu were employees of Evidera at the time this research was conducted.

Author contributions

Shien Guo, Dennis Getsios, Nikhil Revankar, Peng Xu, Gwilym Thompson, Loretto Lacey, Joel Bobula and Maren Gaudig participated in the design of the model, identification of data sources, conduct of data analyses, and implementing the design. Each author also contributed to the interpretation of data and results, drafting the manuscript, and approved the final version. Shien Guo will serve as guarantor for the overall content of the manuscript.

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Authors

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Correspondence to Shien Guo.

Additional information

L. Lacey was formerly affiliated with Janssen Alzheimer Immunotherapy, Dublin, Ireland and M. Gaudig was formerly affiliated with Alzheimer Immunotherapy, Dublin, Ireland.

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Guo, S., Getsios, D., Revankar, N. et al. Evaluating Disease-Modifying Agents: A Simulation Framework for Alzheimer’s Disease. PharmacoEconomics 32, 1129–1139 (2014). https://doi.org/10.1007/s40273-014-0203-5

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  • DOI: https://doi.org/10.1007/s40273-014-0203-5

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