Using the ROC Curve to Measure Association and Evaluate Prediction Accuracy for a Binary Outcome

This review article addresses the ROC curve and its advantage over the odds ratio to measure the association between a continuous variable and a binary outcome. A simple parametric model under the normality assumption and the method of Box-Cox transformation for non-normal data are discussed. Applications of the binormal model and the Box-Cox transformation under both univariate and multivariate inference are illustrated by a comprehensive data analysis tutorial. Finally, a summary and recommendations are given as to the usage of the binormal ROC curve

ThresholdROC: optimum threshold estimation tools for continuous diagnostic tests in R

We introduce an R package that estimates decision thresholds in diagnostic settings with a continuous marker and two or three underlying states. The package implements parametric and non-parametric estimation methods based on minimizing an overall cost function, as well as confidence interval estimation approaches to account for the sampling variability of the cut-off. Further features of the package include sample size determination and estimation of diagnostic accuracy measures. We used randomly generated data and two real datasets to illustrate the capabilities and characteristics of the package

Proportional odds ratio model for comparison of diagnostic tests in meta-analysis

BACKGROUND: Consider a meta-analysis where a 'head-to-head' comparison of diagnostic tests for a disease of interest is intended. Assume there are two or more tests available for the disease, where each test has been studied in one or more papers. Some of the papers may have studied more than one test, hence the results are not independent. Also the collection of tests studied may change from one paper to the other, hence incomplete matched groups. METHODS: We propose a model, the proportional odds ratio (POR) model, which makes no assumptions about the shape of OR(p), a baseline function capturing the way OR changes across papers. The POR model does not assume homogeneity of ORs, but merely specifies a relationship between the ORs of the two tests. One may expand the domain of the POR model to cover dependent studies, multiple outcomes, multiple thresholds, multi-category or continuous tests, and individual-level data. RESULTS: In the paper we demonstrate how to formulate the model for a few real examples, and how to use widely available or popular statistical software (like SAS, R or S-Plus, and Stata) to fit the models, and estimate the discrimination accuracy of tests. Furthermore, we provide code for converting ORs into other measures of test performance like predictive values, post-test probabilities, and likelihood ratios, under mild conditions. Also we provide code to convert numerical results into graphical ones, like forest plots, heterogeneous ROC curves, and post test probability difference graphs. CONCLUSIONS: The flexibility of POR model, coupled with ease with which it can be estimated in familiar software, suits the daily practice of meta-analysis and improves clinical decision-making

Bayesian Bootstrap Inference for the ROC Surface

Accurate diagnosis of disease is of great importance in clinical practice and medical research. The receiver operating characteristic (ROC) surface is a popular tool for evaluating the discriminatory ability of continuous diagnostic test outcomes when there exist three ordered disease classes (e.g., no disease, mild disease, advanced disease). We propose the Bayesian bootstrap, a fully nonparametric method, for conducting inference about the ROC surface and its functionals, such as the volume under the surface. The proposed method is based on a simple, yet interesting, representation of the ROC surface in terms of placement variables. Results from a simulation study demonstrate the ability of our method to successfully recover the true ROC surface and to produce valid inferences in a variety of complex scenarios. An application to data from the Trail Making Test to assess cognitive impairment in Parkinson's disease patients is provided

Efficacy and cost-effectiveness of a physiotherapy program for chronic rotator cuff pathology: A protocol for a randomised, double-blind, placebo-controlled trial

<p>Abstract</p> <p>Background</p> <p>Chronic rotator cuff pathology (CRCP) is a common shoulder condition causing pain and disability. Physiotherapy is often the first line of management for CRCP yet there is little conclusive evidence to support or refute its effectiveness and no formal evaluation of its cost-effectiveness.</p> <p>Methods/Design</p> <p>This randomised, double-blind, placebo-controlled trial will involve 200 participants with CRCP recruited from medical practices, outpatient departments and the community via print and radio media. Participants will be randomly allocated to a physiotherapy or placebo group using concealed allocation stratified by treating physiotherapist. Both groups will receive 10 sessions of individual standardised treatment over 10 weeks from one of 10 project physiotherapists. For the following 12 weeks, the physiotherapy group will continue a home exercise program and the placebo group will receive no treatment. The physiotherapy program will comprise shoulder joint and spinal mobilisation, soft tissue massage, postural taping, and home exercises for scapular control, posture and rotator cuff strengthening. The placebo group will receive inactive ultrasound and gentle application of an inert gel over the shoulder region. Blinded assessment will be conducted at baseline and at 10 weeks and 22 weeks after randomisation. The primary outcome measures are self reported questionnaires including the shoulder pain and disability index (SPADI), average pain on an 11-point numeric rating scale and participant perceived global rating of change. Secondary measures include Medical Outcomes Study 36-item short form (SF-36), Assessment of Quality of Life index, numeric rating scales for shoulder pain and stiffness, participant perceived rating of change for pain, strength and stiffness, and manual muscle testing for shoulder strength using a handheld dynamometer. To evaluate cost-effectiveness, participants will record the use of all health-related treatments in a log-book returned to the assessor monthly. To test the effect of the intervention using an intention-to-treat analysis, linear regression modelling will be applied adjusting for baseline outcome values and other demographic characteristics. Participant measures of perceived change will be compared between groups by calculating the relative risks and their 95% confidence intervals at each time point using log binomial regression.</p> <p>Discussion</p> <p>Results from this trial will contribute to the evidence regarding the effectiveness of a physiotherapy program for the management of CRCP.</p> <p>Trial registration</p> <p>NIH Clinical Trials Registry # NCT00415441</p