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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/2284

Title: A tree based lack-of-fit test for multiple logistic regression
Authors: MOONS, Elke
WETS, Geert
Issue Date: 2004
Citation: STATISTICS IN MEDICINE, 23(9). p. 1425-1438
Abstract: Several omnibus tests have been developed to assess the fit of a regression model. But many of these lack-of-fit tests focus on the simple regression setting. Here, we focus on multiple logistic regression. Pearson's well-known chi-square test statistic and the deviance statistic are no longer valid in the case that the model contains one or more continuous covariates. To overcome this difficulty, Hosmer and Lemeshow proposed a Pearson type statistic based on groups defined by the so-called deciles of risk. We propose a test statistic that is similar in approach to the Hosmer and Lemeshow statistic in that the observations are classified into distinct groups. In the procedure proposed here however, the grouping is not according to probabilities fitted under the null model. We use a recursive partitioning algorithm to divide the sample space into different groups. This generally allows for a more powerful assessment of the model fit. Simulations are carried out to compare the results of the proposed test to that of Hosmer and Lemeshow. Three data examples illustrate the performance of the tree based lack-of-fit test, in comparison to several other tests. Copyright (C) 2004 John Wiley Sons, Ltd.
Notes: Limburgs Univ Ctr, Data Anal & Modeling Grp, B-3590 Diepenbeek, Belgium. Limburgs Univ Ctr, Ctr Stat, B-3590 Diepenbeek, Belgium.Moons, E, Limburgs Univ Ctr, Data Anal & Modeling Grp, Univ Campus Gebouw D, B-3590 Diepenbeek, Belgium.elke.moons@luc.ac.be
URI: http://hdl.handle.net/1942/2284
DOI: 10.1002/sim.1750
ISI #: 000221118500006
ISSN: 0277-6715
Category: A1
Type: Journal Contribution
Validation: ecoom, 2005
Appears in Collections: Research publications

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