Flexible regression models for ROC and risk analysis, with or without a gold standard

Jara

Nonparametric Bayesian Inference in Biostatistics

2015

The development of medical diagnostic tests is of great importance in clinical practice, public health, and medical research. The receiver operating characteristic (ROC) curve is a popular tool for evaluating the accuracy of such tests. We review Bayesian nonparametric methods based on Dirichlet process mixtures and the Bayesian bootstrap for ROC curve estimation and regression. The methods are illustrated by means of data concerning diagnosis of lung cancer in women.

Section: Roc Curvesmentioning

confidence: 99%

Bayesian Nonparametric Approaches for ROC Curve Inference

Jara

Nonparametric Bayesian Inference in Biostatistics

2015

“…Recent work on the latter includes the DP mixture (DPM) model-based approach of Erkanli et al (2006), the Bayesian bootstrap ROC curve estimator of Gu et al (2008), and the stochastic ordering approach of Hanson et al (2008a). Finally, Branscum et al (2014) proposed a method based on mixtures of finite Polya trees to model ROC regression data when there is not a gold standard test available. Additionally, Inácio et al (2011) proposed the use of mixtures of finite Polya trees to model the ROC surface for problems where the patients have to be classified into one of three ordered classes.…”

Section: Roc Curvesmentioning

confidence: 99%

Nonparametric Bayesian Inference in Biostatistics

Mitra

Müeller²

2015

We briefly review some of the nonparametric Bayesians models that are most widely used in biostatistics and bioinformatics. We define the Dirichlet process, Dirichlet process mixtures, the Polya tree, the dependent Dirichlet process and the Gaussian process prior. These few models and variations cover a major part of the models that are used in the literature. The discussion includes references to variations of the basic models that are defined in the chapters of this volume.

“…Finally, a gold standard for assessing some attributes might not be readily available (eg, a standard for suicide ideation). In lieu of a gold standard, 4 broad alternative methods can be used: regression approaches (12,13), simulation (14), capture–recapture methods (15), and network scale-up methods (16). The working group made modifications or revisions to the existing attributes of public health surveillance system evaluation and added 2 attributes (population coverage and informatics capabilities).…”

Section: Introductionmentioning

confidence: 99%

Evaluating Behavioral Health Surveillance Systems

Azofeifa

Stroup²,

Lyerla

et al. 2018

Prev. Chronic Dis.

In 2015, more than 27 million people in the United States reported that they currently used illicit drugs or misused prescription drugs, and more than 66 million reported binge drinking during the previous month. Data from public health surveillance systems on drug and alcohol abuse are crucial for developing and evaluating interventions to prevent and control such behavior. However, public health surveillance for behavioral health in the United States has been hindered by organizational issues and other factors. For example, existing guidelines for surveillance evaluation do not distinguish between data systems that characterize behavioral health problems and those that assess other public health problems (eg, infectious diseases). To address this gap in behavioral health surveillance, we present a revised framework for evaluating behavioral health surveillance systems. This system framework builds on published frameworks and incorporates additional attributes (informatics capabilities and population coverage) that we deemed necessary for evaluating behavioral health–related surveillance. This revised surveillance evaluation framework can support ongoing improvements to behavioral health surveillance systems and ensure their continued usefulness for detecting, preventing, and managing behavioral health problems.