Applicability Analysis of Validation Evidence for Biomedical Computational Models
- Food and Drug Administration (FDA), Silver Spring, MD (United States). Office of Science and Engineering Laboratories (OSEL). Center for Devices and Radiological Health (CDRH)
- Food and Drug Administration (FDA), Silver Spring, MD (United States). Office of Science and Engineering Laboratories (OSEL). Center for Devices and Radiological Health (CDRH)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Computational modeling has the potential to revolutionize medicine the way it transformed engineering. However, despite decades of work, there has only been limited progress to successfully translate modeling research to patient care. One major difficulty which often occurs with biomedical computational models is an inability to perform validation in a setting that closely resembles how the model will be used. For example, for a biomedical model that makes in vivo clinically relevant predictions, direct validation of predictions may be impossible for ethical, technological, or financial reasons. Unavoidable limitations inherent to the validation process lead to challenges in evaluating the credibility of biomedical model predictions. Therefore, when evaluating biomedical models, it is critical to rigorously assess applicability, that is, the relevance of the computational model, and its validation evidence to the proposed context of use (COU). However, there are no well-established methods for assessing applicability. In this paper, we present a novel framework for performing applicability analysis and demonstrate its use with a medical device computational model. The framework provides a systematic, step-by-step method for breaking down the broad question of applicability into a series of focused questions, which may be addressed using supporting evidence and subject matter expertise. The framework can be used for model justification, model assessment, and validation planning. While motivated by biomedical models, it is relevant to a broad range of disciplines and underlying physics. Finally, the proposed applicability framework could help overcome some of the barriers inherent to validation of, and aid clinical implementation of, biomedical models.
- Research Organization:
- Food and Drug Administration (FDA), Silver Spring, MD (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE; Food and Drug Administration (FDA) (United States)
- Grant/Contract Number:
- AC04-94AL85000
- OSTI ID:
- 1399491
- Report Number(s):
- SAND2017-2092J; 651131
- Journal Information:
- Journal of Verification, Validation and Uncertainty Quantification, Vol. 2, Issue 2; ISSN 2377-2158
- Publisher:
- ASMECopyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
MFIX-DEM Phi: Performance and Capability Improvements Towards Industrial Grade Open-source DEM Framework with Integrated Uncertainty Quantification
Annual Report: Fuels (30 September 2012)