A resample-replace lasso procedure for combining high-dimensional markers with limit of detection.

In disease screening, a biomarker combination developed by combining multiple markers tends to have a higher sensitivity than an individual marker. Parametric methods for marker combination rely on the inverse of covariance matrices, which is often a non-trivial problem for high-dimensional data generated by modern high-throughput technologies. Additionally, another common problem in disease diagnosis is the existence of limit of detection (LOD) for an instrument - that is, when a biomarker's value falls below the limit, it cannot be observed and is assigned an NA value.

A ROC-based test for evaluating the group difference with an application to neonatal audiology screening.

This article proposes a powerful method to compare two samples. The proposed method handles comparison of data by drawing inference from ROC curve model parameters. The method estimates parameters from a linear model framework on the empirical sensitivities and specificities.

Group sequential comparison of positive predictive value curves for correlated biomarker data.

Clinical studies of predictive diagnostic tests consider the evaluation of a single test and comparison of two tests regarding their predictive accuracy of disease status. The positive predictive value (PPV) curve is used for assessing the probability of predicting the disease given a positive test result. The sequential property of one PPV curve had been studied.

Order-restricted inference for clustered ROC data with application to fingerprint matching accuracy.

Receiver operating characteristic (ROC) curve is commonly used to evaluate and compare the accuracy of classification methods or markers. Estimating ROC curves has been an important problem in various fields including biometric recognition and diagnostic medicine. In real applications, classification markers are often developed under two or more ordered conditions, such that a natural stochastic ordering exists among the observations.

A cluster-adjusted rank-based test for a clinical trial concerning multiple endpoints with application to dietary intervention assessment.

Multiple endpoints are often naturally clustered based on their scientific interpretations. Tests that compare these clustered outcomes between independent groups may lose efficiency if the cluster structures are not properly accounted for. For the two-sample generalized Behrens-Fisher hypothesis concerning multiple endpoints we propose a cluster-adjusted multivariate test procedure for the comparison and demonstrate its gain in efficiency over test procedures that ignore the clusters.

GATE: an efficient procedure in study of pleiotropic genetic associations.

Background: The association studies on human complex traits are admittedly propitious to identify deleterious genetic markers. Compared to single-trait analyses, multiple-trait analyses can arguably make better use of the information on both traits and markers, and thus improve statistical power of association tests prominently. Principal component analysis (PCA) is a well-known useful tool in multivariate analysis and can be applied to this task.