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

Statistical reproducibility for pairwise t-tests in pharmaceutical research

This paper investigates statistical reproducibility of the t-test. We formulate reproducibility as a predictive inference problem and apply the nonparametric predictive inference (NPI) method. Within our research framework, statistical reproducibility provides inference on the probability that the same test outcome would be reached, if the test were repeated under identical conditions. We present an NPI algorithm to calculate the reproducibility of the t-test and then use simulations to explore the reproducibility both under the null and alternative hypotheses. We then apply NPI reproducibility to a real life scenario of a preclinical experiment, which involves multiple pairwise comparisons of test groups, where different groups are given a different concentration of a drug. The aim of the experiment is to decide the concentration of the drug which is most effective. In both simulations and the application scenario, we study the relationship between reproducibility and two test statistics, the Cohen’s d and the p-value. We also compare the reproducibility of the t-test with the reproducibility of the Wilcoxon Mann-Whitney test. Finally, we examine reproducibility for the final decision of choosing a particular dose in the multiple pairwise comparisons scenario. This paper presents advances on the topic of test reproducibility with relevance for tests used in pharmaceutical research

Nonparametric Predictive Inference for Binary Diagnostic Tests

Measuring the accuracy of diagnostic tests is crucial in many application areas, including medicine, health care, and data mining. Good methods for determining diagnostic accuracy provide useful guidance on selection of patient treatment, and the ability to compare different diagnostic tests has a direct impact on quality of care. In this paper nonparametric predictive inference (NPI) for accuracy of diagnostic tests with binary test results is presented and discussed, together with methods for comparison of two such tests. NPI does not aim at inference for an entire population but instead explicitly considers future observations, which is particularly suitable for inference to support decisions on medical diagnosis for one future patient, or for a predetermined number of future patients, so the NPI approach provides an attractive alternative to standard methods