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Title: Applicability Analysis of Validation Evidence for Biomedical Computational Models

Abstract

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 frameworkmore » 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.« less

Authors:
 [1];  [2];  [3];  [2]
  1. Food and Drug Administration (FDA), Silver Spring, MD (United States). Office of Science and Engineering Laboratories (OSEL). Center for Devices and Radiological Health (CDRH)
  2. Food and Drug Administration (FDA), Silver Spring, MD (United States). Office of Science and Engineering Laboratories (OSEL). Center for Devices and Radiological Health (CDRH)
  3. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Food and Drug Administration (FDA), Silver Spring, MD (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE; Food and Drug Administration (FDA) (United States)
OSTI Identifier:
1399491
Report Number(s):
SAND2017-2092J
Journal ID: ISSN 2377-2158; 651131
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Verification, Validation and Uncertainty Quantification
Additional Journal Information:
Journal Volume: 2; Journal Issue: 2; Journal ID: ISSN 2377-2158
Publisher:
ASME
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; computer simulation; simulation; stents; biomedicine

Citation Formats

Pathmanathan, Pras, Gray, Richard A., Romero, Vicente J., and Morrison, Tina M. Applicability Analysis of Validation Evidence for Biomedical Computational Models. United States: N. p., 2017. Web. doi:10.1115/1.4037671.
Pathmanathan, Pras, Gray, Richard A., Romero, Vicente J., & Morrison, Tina M. Applicability Analysis of Validation Evidence for Biomedical Computational Models. United States. doi:10.1115/1.4037671.
Pathmanathan, Pras, Gray, Richard A., Romero, Vicente J., and Morrison, Tina M. Thu . "Applicability Analysis of Validation Evidence for Biomedical Computational Models". United States. doi:10.1115/1.4037671. https://www.osti.gov/servlets/purl/1399491.
@article{osti_1399491,
title = {Applicability Analysis of Validation Evidence for Biomedical Computational Models},
author = {Pathmanathan, Pras and Gray, Richard A. and Romero, Vicente J. and Morrison, Tina M.},
abstractNote = {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.},
doi = {10.1115/1.4037671},
journal = {Journal of Verification, Validation and Uncertainty Quantification},
number = 2,
volume = 2,
place = {United States},
year = {Thu Sep 07 00:00:00 EDT 2017},
month = {Thu Sep 07 00:00:00 EDT 2017}
}

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