Modeling the Impact of Multicancer Early Detection Tests: A Review of Natural History of Disease Models

Olena Mandrik, Sophie Whyte, Natalia Kunst, Annabel Rayner, Melissa Harden, Sofia Dias, Katherine Payne, Stephen Palmer and Marta O. Soares
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Olena Mandrik: Sheffield Centre for Health and Related Research, University of Sheffield, Sheffield, UK
Sophie Whyte: Sheffield Centre for Health and Related Research, University of Sheffield, Sheffield, UK
Natalia Kunst: Centre for Health Economics, University of York, York, UK
Annabel Rayner: Sheffield Centre for Health and Related Research, University of Sheffield, Sheffield, UK
Melissa Harden: Centre for Reviews and Dissemination, University of York, York, UK
Sofia Dias: Centre for Reviews and Dissemination, University of York, York, UK
Katherine Payne: Manchester Centre for Health Economics, School of Health Sciences, The University of Manchester, UK
Stephen Palmer: Centre for Health Economics, University of York, York, UK
Marta O. Soares: Centre for Health Economics, University of York, York, UK

Medical Decision Making, 2025, vol. 45, issue 8, 1013-1024

Abstract: Introduction The potential for multicancer early detection (MCED) tests to detect cancer at earlier stages is currently being evaluated in screening clinical trials. Once trial evidence becomes available, modeling will be necessary to predict the effects on final outcomes (benefits and harms), account for heterogeneity in determining clinical and cost-effectiveness, and explore alternative screening program specifications. The natural history of disease (NHD) component will use statistical, mathematical, or calibration methods. This work aims to identify, review, and critically appraise the existing literature for alternative modeling approaches proposed for MCED that include an NHD component. Methods Modeling approaches for MCED screening that include an NHD component were identified from the literature, reviewed, and critically appraised. Purposively selected (non-MCED) cancer-screening models were also reviewed. The appraisal focused on the scope, data sources, evaluation approaches, and the structure and parameterization of the models. Results Five different MCED models incorporating an NHD component were identified and reviewed, alongside 4 additional (non-MCED) models. The critical appraisal highlighted several features of this literature. In the absence of trial evidence, MCED effects are based on predictions derived from test accuracy. These predictions rely on simplifying assumptions with unknown impacts, such as the stage-shift assumption used to estimate mortality impacts from predicted stage shifts. None of the MCED models fully characterized uncertainty in the NHD or examined uncertainty in the stage-shift assumption. Conclusion There is currently no modeling approach for MCEDs that can integrate clinical study evidence. In support of policy, it is important that efforts are made to develop models that make the best use of data from the large and costly clinical studies being designed and implemented across the globe. Highlights In the absence of trial evidence, published estimates of the effects of multicancer early detection (MCED) tests are based on predictions derived from test accuracy. These predictions rely on simplifying assumptions, such as the stage-shift assumption used to estimate mortality effects from predicted stage shifts. The effects of such simplifying assumptions are mostly unknown. None of the existing MCED models fully characterize uncertainty in the natural history of disease; none examine uncertainty in the stage-shift assumption. Currently, there is no modeling approach that can integrate clinical study evidence.

Keywords: Multicancer early detection; natural history of disease; models; calibration (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:45:y:2025:i:8:p:1013-1024

DOI: 10.1177/0272989X251351639

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