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Increasing the power: A practical approach to goodness-of-fit test for logistic regression models with continuous predictors

Publication ,  Journal Article
Xie, XJ; Pendergast, J; Clarke, W
Published in: Computational Statistics and Data Analysis
January 20, 2008

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. © 2007 Elsevier B.V. All rights reserved.

Duke Scholars

Published In

Computational Statistics and Data Analysis

DOI

ISSN

0167-9473

Publication Date

January 20, 2008

Volume

52

Issue

5

Start / End Page

2703 / 2713

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1403 Econometrics
  • 0802 Computation Theory and Mathematics
  • 0104 Statistics
 

Citation

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Xie, X. J., Pendergast, J., & Clarke, W. (2008). Increasing the power: A practical approach to goodness-of-fit test for logistic regression models with continuous predictors. Computational Statistics and Data Analysis, 52(5), 2703–2713. https://doi.org/10.1016/j.csda.2007.09.027
Xie, X. J., J. Pendergast, and W. Clarke. “Increasing the power: A practical approach to goodness-of-fit test for logistic regression models with continuous predictors.” Computational Statistics and Data Analysis 52, no. 5 (January 20, 2008): 2703–13. https://doi.org/10.1016/j.csda.2007.09.027.
Xie XJ, Pendergast J, Clarke W. Increasing the power: A practical approach to goodness-of-fit test for logistic regression models with continuous predictors. Computational Statistics and Data Analysis. 2008 Jan 20;52(5):2703–13.
Xie, X. J., et al. “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, no. 5, Jan. 2008, pp. 2703–13. Scopus, doi:10.1016/j.csda.2007.09.027.
Xie XJ, Pendergast J, Clarke W. Increasing the power: A practical approach to goodness-of-fit test for logistic regression models with continuous predictors. Computational Statistics and Data Analysis. 2008 Jan 20;52(5):2703–2713.
Journal cover image

Published In

Computational Statistics and Data Analysis

DOI

ISSN

0167-9473

Publication Date

January 20, 2008

Volume

52

Issue

5

Start / End Page

2703 / 2713

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1403 Econometrics
  • 0802 Computation Theory and Mathematics
  • 0104 Statistics