A COMPARISON OF GOODNESS‐OF‐FIT TESTS FOR THE LOGISTIC REGRESSION MODEL

A COMPARISON OF GOODNESS‐OF‐FIT TESTS FOR THE LOGISTIC REGRESSION MODEL Recent work has shown that there may be disadvantages in the use of the chi‐square‐like goodness‐of‐fit tests for the logistic regression model proposed by Hosmer and Lemeshow that use fixed groups of the estimated probabilities. A particular concern with these grouping strategies based on estimated probabilities, fitted values, is that groups may contain subjects with widely different values of the covariates. It is possible to demonstrate situations where one set of fixed groups shows the model fits while the test rejects fit using a different set of fixed groups. We compare the performance by simulation of these tests to tests based on smoothed residuals proposed by le Cessie and Van Houwelingen and Royston, a score test for an extended logistic regression model proposed by Stukel, the Pearson chi‐square and the unweighted residual sum‐of‐ squares. These simulations demonstrate that all but one of Royston's tests have the correct size. An examination of the performance of the tests when the correct model has a quadratic term but a model containing only the linear term has been fit shows that the Pearson chi‐square, the unweighted sum‐of‐squares, the Hosmer–Lemeshow decile of risk, the smoothed residual sum‐of‐squares and Stukel's score test, have power exceeding 50 per cent to detect moderate departures from linearity when the sample size is 100 and have power over 90 per cent for these same alternatives for samples of size 500. All tests had no power when the correct model had an interaction between a dichotomous and continuous covariate but only the continuous covariate model was fit. Power to detect an incorrectly specified link was poor for samples of size 100. For samples of size 500 Stukel's score test had the best power but it only exceeded 50 per cent to detect an asymmetric link function. The power of the unweighted sum‐of‐squares test to detect an incorrectly specified link function was slightly less than Stukel's score test. We illustrate the tests within the context of a model for factors associated with low birth weight. © 1997 by John Wiley & Sons, Ltd. Stat. Med., Vol. 16, 965–980 (1997). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistics in Medicine Wiley

A COMPARISON OF GOODNESS‐OF‐FIT TESTS FOR THE LOGISTIC REGRESSION MODEL

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Publisher
Wiley
Copyright
Copyright © 1997 John Wiley & Sons, Ltd.
ISSN
0277-6715
eISSN
1097-0258
DOI
10.1002/(SICI)1097-0258(19970515)16:9<965::AID-SIM509>3.0.CO;2-O
Publisher site
See Article on Publisher Site

Abstract

Recent work has shown that there may be disadvantages in the use of the chi‐square‐like goodness‐of‐fit tests for the logistic regression model proposed by Hosmer and Lemeshow that use fixed groups of the estimated probabilities. A particular concern with these grouping strategies based on estimated probabilities, fitted values, is that groups may contain subjects with widely different values of the covariates. It is possible to demonstrate situations where one set of fixed groups shows the model fits while the test rejects fit using a different set of fixed groups. We compare the performance by simulation of these tests to tests based on smoothed residuals proposed by le Cessie and Van Houwelingen and Royston, a score test for an extended logistic regression model proposed by Stukel, the Pearson chi‐square and the unweighted residual sum‐of‐ squares. These simulations demonstrate that all but one of Royston's tests have the correct size. An examination of the performance of the tests when the correct model has a quadratic term but a model containing only the linear term has been fit shows that the Pearson chi‐square, the unweighted sum‐of‐squares, the Hosmer–Lemeshow decile of risk, the smoothed residual sum‐of‐squares and Stukel's score test, have power exceeding 50 per cent to detect moderate departures from linearity when the sample size is 100 and have power over 90 per cent for these same alternatives for samples of size 500. All tests had no power when the correct model had an interaction between a dichotomous and continuous covariate but only the continuous covariate model was fit. Power to detect an incorrectly specified link was poor for samples of size 100. For samples of size 500 Stukel's score test had the best power but it only exceeded 50 per cent to detect an asymmetric link function. The power of the unweighted sum‐of‐squares test to detect an incorrectly specified link function was slightly less than Stukel's score test. We illustrate the tests within the context of a model for factors associated with low birth weight. © 1997 by John Wiley & Sons, Ltd. Stat. Med., Vol. 16, 965–980 (1997).

Journal

Statistics in MedicineWiley

Published: May 15, 1997

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