Goodness-of-Fit Tests for Logistic Regression

• Activity Number: 359
• Type: Contributed
• Date/Time: Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Sutan Wu*+ and Dan McGee
• Companies: Florida State University and Florida State University
• Keywords: goodness-of-fit test ; logistic regression ; generalized linear model

Abstract

The generalized linear model and particularly the logistic model are widely used in public health, medicine, and epidemiology. Goodness-of-fit tests for these models are popularly used to describe how well a proposed model fits a set of observations. These different goodness-of-fit tests all have individual advantages and disadvantages. In this poster, we mainly consider the performance of the ``Hosmer-Lemeshow" test, the Pearson's chi-square test, the unweighted sum of squares test and the cumulative residual test. We examined their performance in a series of empirical studies as well as simulation scenarios. We conclude that the cumulative sums of residuals test gives better overall performance than the other three. We also conclude that the commonly suggested practice of assuming that a p-value less than 0.15 is an indication of lack of fit at the initial steps of model diagnostics should be adopted. Additionally, D'Agostino et al. presented the relationship of the stacked logistic regression and the Cox regression model in the Framingham Heart Study. So in our future study, we will examine the possibility and feasibility of the adaption these goodness-of-fit tests to the Cox pr