Related Experiment Video
Updated: Jul 10, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Semi-parametric estimation of the binormal ROC curve for a continuous diagnostic test
Tianxi Cai1, Chaya S Moskowitz
1Department of Biostatistics, Harvard University, Boston, MA 02115, USA. tcai@hsph.harvard.edu
Abstract:
Not until recently has much attention been given to deriving maximum likelihood methods for estimating the intercept and slope parameters from a binormal ROC curve that assesses the accuracy of a continuous diagnostic test. We propose two new methods for estimating these parameters. The first method uses the profile likelihood and a simple algorithm to produce fully efficient estimates. The second method is based on a pseudo-maximum likelihood that can easily accommodate adjusting for covariates that could affect the accuracy of the continuous test.
More Related Videos
Related Concept Videos
Introduction to Nonparametric Statistics
One of...
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with data...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Receiver Operating Characteristic Plot
The Mantel-Cox Log-Rank Test
Introduction to Normal Distributions

