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Testing the fit of the logistic model for matched case-control studies

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  • Chen, Li-Ching
  • Wang, Jiun-Yi

Abstract

With numerous statistical packages being easily available to conduct the logistic regression analysis, assessment for the goodness-of-fit in the logistic case-control studies becomes more important in practice. While various methods for model checking in conventional case-control studies have been proposed in the literature, methods for checking model adequacy with matched case-control data get relatively less attention. In this study, we propose an omnibus goodness-of-fit test to assess adequacy of the conditional logistic model for matched case-control data. The proposed test can be either constructed based on the discrepancy between two moment estimations or derived to be a score-type test under a general random-effects model. Computation of the proposed test is quite simple in which it does not need to partition the covariate space or to estimate p-value of the test via simulations. The asymptotic null distribution and power calculation of the test are derived under a sequence of alternatives. Empirical type I error rates and powers of the test are performed by simulation studies. An example has been used to illustrate the proposed method as well.

Suggested Citation

  • Chen, Li-Ching & Wang, Jiun-Yi, 2013. "Testing the fit of the logistic model for matched case-control studies," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 309-319.
  • Handle: RePEc:eee:csdana:v:57:y:2013:i:1:p:309-319
    DOI: 10.1016/j.csda.2012.07.001
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    References listed on IDEAS

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    1. White,Halbert, 1994. "Estimation, Inference and Specification Analysis," Cambridge Books, Cambridge University Press, number 9780521252805.
    2. Newey, Whitney K, 1985. "Maximum Likelihood Specification Testing and Conditional Moment Tests," Econometrica, Econometric Society, vol. 53(5), pages 1047-1070, September.
    3. White,Halbert, 1996. "Estimation, Inference and Specification Analysis," Cambridge Books, Cambridge University Press, number 9780521574464.
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    Cited by:

    1. Li‐Ching Chen & Jiun‐Yi Wang, 2020. "Discussion of “Assessing the goodness‐of‐fit of logistic regression models in large samples: A modification of the Hosmer‐Lemeshow test,” by Giovanni Nattino, Michael L. Pennell, and Stanley Lemeshow," Biometrics, The International Biometric Society, vol. 76(2), pages 569-571, June.

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