32 research outputs found
Pencina et al. Respond to "The Incremental Value of New Markers" and "Clinically Relevant Measures? A Note of Caution"
The effects of comorbidity on the onset and persistence of generalized anxiety disorder in the ICPE surveys.
GesondheidswetenskappePsigiatriePlease help us populate SUNScholar with the post print version of this article. It can be e-mailed to: [email protected]
Prediction of kidney failure in children with chronic kidney disease and obstructive uropathy
Establishment and Validation of Extra-transitional Zone Prostate Specific Antigen Density (ETzD), a Novel Structure-based Parameter for Quantifying the Oncological Hazard of Prostates with Enlarged Stroma
Predicting technical debt from commit contents: reproduction and extension with automated feature selection
Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable
<p>Abstract</p> <p>Background</p> <p>When outcomes are binary, the c-statistic (equivalent to the area under the Receiver Operating Characteristic curve) is a standard measure of the predictive accuracy of a logistic regression model.</p> <p>Methods</p> <p>An analytical expression was derived under the assumption that a continuous explanatory variable follows a normal distribution in those with and without the condition. We then conducted an extensive set of Monte Carlo simulations to examine whether the expressions derived under the assumption of binormality allowed for accurate prediction of the empirical c-statistic when the explanatory variable followed a normal distribution in the combined sample of those with and without the condition. We also examine the accuracy of the predicted c-statistic when the explanatory variable followed a gamma, log-normal or uniform distribution in combined sample of those with and without the condition.</p> <p>Results</p> <p>Under the assumption of binormality with equality of variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the product of the standard deviation of the normal components (reflecting more heterogeneity) and the log-odds ratio (reflecting larger effects). Under the assumption of binormality with unequal variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the standardized difference of the explanatory variable in those with and without the condition. In our Monte Carlo simulations, we found that these expressions allowed for reasonably accurate prediction of the empirical c-statistic when the distribution of the explanatory variable was normal, gamma, log-normal, and uniform in the entire sample of those with and without the condition.</p> <p>Conclusions</p> <p>The discriminative ability of a continuous explanatory variable cannot be judged by its odds ratio alone, but always needs to be considered in relation to the heterogeneity of the population.</p