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A Chi-Squared Goodness-of-Fit Test for Logistic Regression Models Based on Case-Control Data

Biao Zhang
Biometrika
Vol. 86, No. 3 (Sep., 1999), pp. 531-539
Published by: Oxford University Press on behalf of Biometrika Trust
Stable URL: http://www.jstor.org/stable/2673652
Page Count: 9
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
A Chi-Squared Goodness-of-Fit Test for Logistic Regression Models Based on Case-Control Data
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Abstract

We propose a chi-squared-type statistic to test the validity of the logistic regression model based on case-control data by adapting the goodness-of-fit test of Nikulin-Rao-Robson-Moore. The proposed test statistic requires a high-dimensional matrix inversion, but is otherwise easy to compute and has an asymptotic chi-squared distribution. This test statistic is an alternate to the Kolmogorov-Smirnov-type statistic of Qin & Zhang (1997) and does not need to employ a bootstrap method to evaluate its critical values. We present some results on simulation and on analysis of two real datasets.

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