Comparison of strategies for validating binary logistic regression models Aaa sex wab com
Logistic regression models are frequently used to determine the association between a set of explanatory variables and a binary or dichotomous outcome variable.
Two key elements in assessing the performance of a fitted logistic regression model are the assessment of model calibration and model discrimination.
One can then estimate the sensitivity and specificity of these classifications. one minus specificity over all possible thresholds.
The area under the ROC curve is equivalent to the c-statistic .
the overall average predicted probability of the event) (). J Clin Epi 8-939 Tjur T (2009): Coefficients of determination in logistic regression models-A new proposal: The coefficient of discrimination.
The first chi-square test is a test of overall calibration accuracy ("calibration in the large"), and the second will also detect errors such as slope shrinkage caused by overfitting or regression to the mean. The goodness of fit test based on the (uncalibrated) Brier score is due to Hilden, Habbema, and Bjerregaard (1978) and is discussed in Spiegelhalter (1986). Frank Harrell Department of Biostatistics, Vanderbilt University [email protected] Harrell FE, Lee KL, Mark DB (1996): Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Harrell FE, Lee KL (1987): Using logistic calibration to assess the accuracy of probability predictions (Technical Report).
Therefore there are two normal distributions: a normal distribution in those subjects with the condition or outcome and a normal distribution in those subjects without the condition or outcome.