Journal article
Increasing the power: A practical approach to goodness-of-fit test for logistic regression models with continuous predictors
Computational Statistics and Data Analysis, Vol.52(5), pp.2703-2713
2008
DOI: 10.1016/j.csda.2007.09.027
Abstract
When continuous predictors are present, classical Pearson and deviance goodness-of-fit tests to assess logistic model fit break down. The Hosmer–Lemeshow test can be used in these situations. While simple to perform and widely used, it does not have desirable power in many cases and provides no further information on the source of any detectable lack of fit. Tsiatis proposed a score statistic to test for covariate regional effects. While conceptually elegant, its lack of a general rule for how to partition the covariate space has, to a certain degree, limited its popularity. We propose a new method for goodness-of-fit testing that uses a very general partitioning strategy (clustering) in the covariate space and either a Pearson statistic or a score statistic. Properties of the proposed statistics are discussed, and a simulation study demonstrates increased power to detect model misspecification in a variety of settings. An application of these different methods on data from a clinical trial illustrates their use. Discussions on further improvement of the proposed tests and extending this new method to other data situations, such as ordinal response regression models are also included.
Details
- Title: Subtitle
- Increasing the power: A practical approach to goodness-of-fit test for logistic regression models with continuous predictors
- Creators
- Xian-Jin Xie - Division of Biostatistics, Department of Clinical Sciences and Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USAJane Pendergast - Department of Biostatistics, The University of Iowa College of Public Health, Iowa City, IA 52242, USAWilliam Clarke - Department of Biostatistics, The University of Iowa College of Public Health, Iowa City, IA 52242, USA
- Resource Type
- Journal article
- Publication Details
- Computational Statistics and Data Analysis, Vol.52(5), pp.2703-2713
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.csda.2007.09.027
- ISSN
- 0167-9473
- eISSN
- 1872-7352
- Language
- English
- Date published
- 2008
- Academic Unit
- Dental Research; Preventive and Community Dentistry; Biostatistics
- Record Identifier
- 9983917677602771
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