Journal
STATISTICAL METHODS IN MEDICAL RESEARCH
Volume 32, Issue 7, Pages 1403-1419Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/09622802231176030
Keywords
Transformation model; receiver operating characteristic curve; area under the receiver operating characteristic curve; diagnostic test; distribution regression; ordinal outcome; censoring; Youden index; overlapping coefficient; limit of detection
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Receiver operating characteristic analysis is widely used for evaluating the accuracy of medical diagnostic tests. However, there is no consensus on a single framework for consistent statistical inference in handling complex medical data. We propose a regression model that accommodates these complexities and provides unbiased estimates with nominal coverage.
Receiver operating characteristic analysis is one of the most popular approaches for evaluating and comparing the accuracy of medical diagnostic tests. Although various methodologies have been developed for estimating receiver operating characteristic curves and their associated summary indices, there is no consensus on a single framework that can provide consistent statistical inference while handling the complexities associated with medical data. Such complexities might include non-normal data, covariates that influence the diagnostic potential of a test, ordinal biomarkers or censored data due to instrument detection limits. We propose a regression model for the transformed test results which exploits the invariance of receiver operating characteristic curves to monotonic transformations and accommodates these features. Simulation studies show that the estimates based on transformation models are unbiased and yield coverage at nominal levels. The methodology is applied to a cross-sectional study of metabolic syndrome where we investigate the covariate-specific performance of weight-to-height ratio as a non-invasive diagnostic test. Software implementations for all the methods described in the article are provided in the tram add-on package to the R system for statistical computing and graphics.
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