Bayesian estimation of the receiver operating characteristic curve for a diagnostic test with a limit of detection in the absence of a gold standard

2010 ◽  
Vol 29 (20) ◽  
pp. 2090-2106 ◽  
Author(s):  
Seyed Reza Jafarzadeh ◽  
Wesley O. Johnson ◽  
Jessica M. Utts ◽  
Ian A. Gardner
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Bayesian Estimation ◽  
Diagnostic Test ◽  
Gold Standard ◽  
Operating Characteristic ◽  
Limit Of Detection ◽  
Characteristic Curve ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Optimal combination of biomarkers for time-dependent receiver operating characteristic estimation and related problems

2014 ◽  
Vol 26 (2) ◽  
pp. 898-913
Author(s):  
Zhong Guan ◽  
Jing Qin
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Diagnostic Test ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Time Dependent ◽  
Likelihood Method ◽  
Test Accuracy ◽  
Operating Characteristic Curve ◽  
Receiver Operating

The receiver operating characteristic curve is commonly used for assessing diagnostic test accuracy and for discriminatory ability of a medical diagnostic test in distinguishing between diseases and non-diseased individuals. With the advance of technology, many genetic variables and biomarker variables are easily collected. The most challenging problem is how to combine clinical, genetic, and biomarker variables together to predict disease status. If one is interested in predicting t-year survival, however, the status of “case” (death) and “control” (survival) at the given t-year is unknown for those individuals who were censored before t-year. To conduct a receiver operating characteristic analysis, one has to impute those ambiguous statuses. In this paper, we study a maximum pseudo likelihood method to estimate the underlying parameters and baseline distribution functions. The proposed approach produces more efficient and smoother estimate of the optimal time-dependent receiver operating characteristic curve and more stable estimation of the prediction rule for the t-year survivors. More importantly, the proposal is equipped with a goodness-of-fit test for the model assumption based on the bootstrap method. Two real medical data sets are used for illustration.

scholarly journals COMPARISION OF BINARY DIAGNOSTIC PREDICTORS USING ENTROPY

2018 ◽  
Vol 6 (1) ◽  
pp. 440-447
Author(s):  
Kathare Alfred ◽  
Otieno Argwings ◽  
Kimeli Victor
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Gold Standard ◽  
Large Scale ◽  
Operating Characteristic ◽  
Standard Procedure ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Operating Characteristic Curve ◽  
Receiver Operating

The use of gold standard procedures in screening may be costly, risky or even unethical. It is, therefore, not admissible for large scale application. In this case, a more acceptable diagnostic predictor is applied to a sample of subjects alongside a gold standard procedure. The performance of the predictor is then evaluated using Receiver Operating Characteristic curve. The area under the curve, then, provides a summative measure of the performance of the predictor. The Receiver Operating Characteristic curve is a trade-off between sensitivity and specificity which in most cases are of different clinical significance. Also, the area under the curve is criticized for lack of coherent interpretation. In this study, we proposed the use of entropy as a summary index measure of uncertainty to compare diagnostic predictors. Noting that a diseased subject who is truly identified with the disease at a lower cut-off will also be identified at a higher cut-off, we substituted time variable in survival analysis for cut-offs in a binary predictor. We then derived the entropy of the functions of diagnostic predictors. Application of the procedure to real data showed that entropy was a strong measure for quantifying the amount of uncertainty engulfed in a set of cut-offs of binary diagnostic predictor.

Bootstrap-based testing approaches for the assessment of the diagnostic accuracy of biomarkers subject to a limit of detection

2018 ◽  
Vol 28 (5) ◽  
pp. 1564-1578
Author(s):  
Alba M Franco-Pereira ◽  
Christos T Nakas ◽  
Alexander B Leichtle ◽  
M Carmen Pardo
Keyword(s):  
Diagnostic Accuracy ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Simulation Study ◽  
Operating Characteristic ◽  
Limit Of Detection ◽  
Characteristic Curve ◽  
Operating Characteristic Curve ◽  
Receiver Operating ◽  
Non Parametric

Assessment of the diagnostic accuracy of biomarkers through receiver operating characteristic curve analysis frequently involves a limit of detection imposed by the laboratory analytical system precision. As a consequence, measurements below a certain level are undetectable and ignoring these is known to lead to negatively biased estimates of the area under the receiver operating characteristic curve. In this article, we introduce two receiver operating characteristic curve-based parametric approaches that tackle the issue of correct assessment of diagnostic markers in the presence of a limit of detection. Proposed approaches are simulation-based utilising bootstrap methodology. Non-parametric alternatives that are naively used in the literature do not solve the inherent problem of limit of detection values which are treated as censored observations. However, the latter seems to perform adequately well in our simulation study. Nonparametric bootstrap was consistently used throughout, while other bootstrap alternatives performed similarly in our pilot simulation study. The simulation study involves the comparison of parametric and non-parametric options described here versus alternative strategies that are routinely used in the literature. We apply all methods to a study-setting resembling a chemical quasi-standard situation, where compound tumour biomarkers were searched within a multi-variable set of measurements to discriminate between two groups, namely colorectal cancer and controls. We focus in the assessment of glutamine and methionine.

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