Significance tests for covariates in the diagnostic accuracy index of a biomarker against a continuous gold standard.

Receiver operating characteristic (ROC) curve is a popular tool to describe and compare the diagnostic accuracy of biomarkers when a binary-scale gold standard is available. However, there are many examples of diagnostic tests whose gold standards are continuous. Hence, Several extensions of receiver operating characteristic (ROC) curve are proposed to evaluate the diagnostic potential of biomarkers when the gold standard is continuous-scale. Moreover, in evaluating these biomarkers, it is often necessary to consider the effects of covariates on the diagnostic accuracy of the biomarker of interest. Covariates may include subject characteristics, expertise of the test operator, test procedures or aspects of specimen handling. Applying the covariate adjustment to the case that the gold standard is continuous is challenging and has not been addressed in the literature. To fill the gap, we propose two general testing frameworks to account for the covariates effect on diagnostic accuracy. Simulation studies are conducted to compare the proposed tests. Data from a study that assessed three types of imaging modalities with the purpose of detecting neoplastic colon polyps and cancers are used to illustrate the proposed methods.

Error-corrected estimation of a diagnostic accuracy index of a biomarker against a continuous gold standard.

This article concerns evaluating the effectiveness of a continuous diagnostic biomarker against a continuous gold standard that is measured with error. Extending the work of Obuchowski (2005, 2016), Wu et al (2016) suggested an accuracy index and proposed an estimator for the index with error-prone standard when the reliability coefficient is known. Combining with additional measurements (without measurement errors) on the continuous gold standard collected from some subjects, this article proposes two adaptive estimators of the accuracy index when the reliability coefficient is unknown, and further establish the consistency and asymptotic normality of these estimators. Simulation studies are conducted to compare various estimators. Data from an intervention trial on glycemic control among children with type 1 diabetes are used to illustrate the proposed methods.

Estimation of an accuracy index of a diagnostic biomarker when the reference biomarker is continuous and measured with error.

New biomarkers continue to be developed for the purpose of diagnosis, and their diagnostic performances are typically compared with an existing reference biomarker used for the same purpose. Considerable amounts of research have focused on receiver operating characteristic curves analysis when the reference biomarker is dichotomous. In the situation where the reference biomarker is measured on a continuous scale and dichotomization is not practically appealing, an index was proposed in the literature to measure the accuracy of a continuous biomarker, which is essentially a linear function of the popular Kendall's tau. We consider the issue of estimating such an accuracy index when the continuous reference biomarker is measured with errors. We first investigate the impact of measurement errors on the accuracy index, and then propose methods to correct for the bias due to measurement errors. Simulation results show the effectiveness of the proposed estimator in reducing biases. The methods are exemplified with hemoglobin A1c measurements obtained from both the central lab and a local lab to evaluate the accuracy of the mean data obtained from the metered blood glucose monitoring against the centrally measured hemoglobin A1c from a behavioral intervention study for families of youth with type 1 diabetes.

Repeated significance tests of linear combinations of sensitivity and specificity of a diagnostic biomarker.

A sequential design is proposed to test whether the accuracy of a binary diagnostic biomarker meets the minimal level of acceptance. The accuracy of a binary diagnostic biomarker is a linear combination of the marker's sensitivity and specificity. The objective of the sequential method is to minimize the maximum expected sample size under the null hypothesis that the marker's accuracy is below the minimal level of acceptance. The exact results of two-stage designs based on Youden's index and efficiency indicate that the maximum expected sample sizes are smaller than the sample sizes of the fixed designs. Exact methods are also developed for estimation, confidence interval and p-value concerning the proposed accuracy index upon termination of the sequential testing. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Exact inference on contrasts in means of intraclass correlation models with missing responses.

Intraclass correlation models with missing data at random are considered. With a properly reduced model, a general method, which allows repeated observations with missing in non-monotone pattern, is proposed to construct exact test statistics and simultaneous confidence intervals for linear contrasts in the means. Simulation results are given to compare exact and asymptotic simultaneous confidence intervals. A real example is provided for illustration of the proposed method.

Estimation of Variance Components in the Mixed-Effects Models: A Comparison Between Analysis of Variance and Spectral Decomposition.

The mixed-effects models with two variance components are often used to analyze longitudinal data. For these models, we compare two approaches to estimating the variance components, the analysis of variance approach and the spectral decomposition approach. We establish a necessary and sufficient condition for the two approaches to yield identical estimates, and some sufficient conditions for the superiority of one approach over the other, under the mean squared error criterion. Applications of the methods to circular models and longitudinal data are discussed. Furthermore, simulation results indicate that better estimates of variance components do not necessarily imply higher power of the tests or shorter confidence intervals.