Statistical Issues in Testing Conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Profile Claims.
Date
2016-04
Journal Title
Journal ISSN
Volume Title
Repository Usage Stats
views
downloads
Citation Stats
Abstract
A major initiative of the Quantitative Imaging Biomarker Alliance is to develop standards-based documents called "Profiles," which describe one or more technical performance claims for a given imaging modality. The term "actor" denotes any entity (device, software, or person) whose performance must meet certain specifications for the claim to be met. The objective of this paper is to present the statistical issues in testing actors' conformance with the specifications. In particular, we present the general rationale and interpretation of the claims, the minimum requirements for testing whether an actor achieves the performance requirements, the study designs used for testing conformity, and the statistical analysis plan. We use three examples to illustrate the process: apparent diffusion coefficient in solid tumors measured by MRI, change in Perc 15 as a biomarker for the progression of emphysema, and percent change in solid tumor volume by computed tomography as a biomarker for lung cancer progression.
Type
Department
Description
Provenance
Citation
Permalink
Published Version (Please cite this version)
Publication Info
Obuchowski, Nancy A, Andrew Buckler, Paul Kinahan, Heather Chen-Mayer, Nicholas Petrick, Daniel P Barboriak, Jennifer Bullen, Huiman Barnhart, et al. (2016). Statistical Issues in Testing Conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Profile Claims. Academic radiology, 23(4). pp. 496–506. 10.1016/j.acra.2015.12.020 Retrieved from https://hdl.handle.net/10161/27330.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
Collections
Scholars@Duke
Huiman Xie Barnhart
My research interests include both statistical methodology and disease-specific clinical research biostatistics. My statistical research areas include methods for outcomes, endpoints, estimands, assessing reliability/agreement between methods or raters, evaluating performance of new medical diagnostic tests, and methods for design of clinical trials. My collaborative research include the following clinical areas: liver injury, cardiovascular imaging, radiology imaging, cardiovascular disease, renal disease, reproductive medicine, Parkinson disease, and aging.
Daniel Carl Sullivan
Research interests are in oncologic imaging, especially the clinical evaluation and validation of imaging biomarkers for therapeutic response assessment.
Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.