Biostatistics and Epidemiology最新文献

This manuscript estimates the area under the receiver operating characteristic curve (AUC) of combined biomarkers in a high-dimensional setting. We propose a penalization approach to the inference of precision matrices in the presence of the limit of detection. A new version of expectation-maximization algorithm is then proposed for the penalized likelihood, with the use of numerical integration and the graphical lasso method. The estimated precision matrix is then applied to the inference of AUCs. The proposed method outperforms the existing methods in numerical studies. We apply the proposed method to a data set of brain tumor study. The results show a higher accuracy on the estimation of AUC compared with the existing methods.

Recent advances in receiver operating characteristic (ROC) curve analyses advocate modeling of placement value (PV), a quantity that measures the position of diseased test scores relative to the healthy population. Compared to traditional approaches, this PV-based alternative works directly with ROC curves and is attractive when assessing covariate effects on, or incorporating a priori constraints of, ROC curves. Several distributions can be used to model the PV, yet little guidelines exist in the literature on which to use. Through extensive simulation studies, we investigate several parametric models for PV when data are generated from a variety of mechanisms. We discuss the pros and cons of each of these models and illustrate their applications with data from a study of prenatal ultrasound examinations and large-for-gestational age birth.

The receiver operating characteristic (ROC) curve displays sensitivity versus 1-specificity over a set of thresholds. The area under the ROC curve (AUC) is a global scalar summary of this curve. In the context of time-dependent ROC methods, we are interested in global scalar measures that summarize sequences of time-dependent AUCs over time. The concordance probability is a candidate for such purposes. The concordance probability can provide a global assessment of the discrimination ability of a test for an event that occurs at random times and may be right censored. If the test adequately differentiates between subjects who survive longer times and those who survive shorter times, this will assist clinical decisions. In this context the concordance probability may support assessment of precision medicine tools based on prognostic biomarkers models for overall survival. Definitions of time-dependent sensitivity and specificity are reviewed. Some connections between such definitions and concordance measures are also reviewed and we establish new connections via new measures of global concordance. We explore the relationship between such measures and their corresponding time-dependent AUC. To illustrate these concepts, an application in the context of Alzheimer's disease is presented.

Many studies have focused on investigating deviations from additive interaction of two dichotomous risk factors on a binary outcome. There is, however, a gap in the literature with respect to interactions on the additive scale of >2 risk factors. In this paper, we present an approach for examining deviations from additive interaction among three or more binary exposures. The relative excess risk due to interaction (RERI) is used as measure of additive interaction. First, we concentrate on three risk factors - we propose to decompose the total RERI to: the RERI owned to the joint presence of all three risk factors and the RERI of any two risk factors, given that the third is absent. We then extend this approach, to >3 binary risk factors. For illustration, we use a sample from data from the Greek EPIC cohort and we investigate the association with overall mortality of Mediterranean diet, body mass index , and smoking. Our formulae enable better interpretability of any evidence for deviations from additivity owned to more than two risk factors and provide simple ways of communicating such results from a public health perspective by attributing any excess relative risk to specific combinations of these factors. Abbreviations: BMI: Body Mass Index; ERR: excess relative risk; EPIC: European Prospective Investigation into Cancer and nutrition; MD: Mediterranean diet; RERI: relative excess risk due to interaction; RR: relative risk; TotRERI: total relative excess risk due to interaction.