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Cutpoint selection for discretizing a continuous covariate for generalized estimating equations

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  • Tunes-da-Silva, Gisela
  • Klein, John P.

Abstract

We consider the problem of dichotomizing a continuous covariate when performing a regression analysis based on a generalized estimation approach. The problem involves estimation of the cutpoint for the covariate and testing the hypothesis that the binary covariate constructed from the continuous covariate has a significant impact on the outcome. Due to the multiple testing used to find the optimal cutpoint, we need to make an adjustment to the usual significance test to preserve the type-I error rates. We illustrate the techniques on one data set of patients given unrelated hematopoietic stem cell transplantation. Here the question is whether the CD34 cell dose given to patient affects the outcome of the transplant and what is the smallest cell dose which is needed for good outcomes.

Suggested Citation

  • Tunes-da-Silva, Gisela & Klein, John P., 2011. "Cutpoint selection for discretizing a continuous covariate for generalized estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 226-235, January.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:1:p:226-235
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    References listed on IDEAS

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    1. Lausen, Berthold & Schumacher, Martin, 1996. "Evaluating the effect of optimized cutoff values in the assessment of prognostic factors," Computational Statistics & Data Analysis, Elsevier, vol. 21(3), pages 307-326, March.
    2. Contal, Cecile & O'Quigley, John, 1999. "An application of changepoint methods in studying the effect of age on survival in breast cancer," Computational Statistics & Data Analysis, Elsevier, vol. 30(3), pages 253-270, May.
    3. John P. Klein & Per Kragh Andersen, 2005. "Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function," Biometrics, The International Biometric Society, vol. 61(1), pages 223-229, March.
    4. Per Kragh Andersen, 2003. "Generalised linear models for correlated pseudo-observations, with applications to multi-state models," Biometrika, Biometrika Trust, vol. 90(1), pages 15-27, March.
    5. Brent R. Logan & John P. Klein & Mei‐Jie Zhang, 2008. "Comparing Treatments in the Presence of Crossing Survival Curves: An Application to Bone Marrow Transplantation," Biometrics, The International Biometric Society, vol. 64(3), pages 733-740, September.
    6. Adin-Cristian Andrei & Susan Murray, 2007. "Regression Models for the Mean of the Quality-of-Life-Adjusted Restricted Survival Time Using Pseudo-Observations," Biometrics, The International Biometric Society, vol. 63(2), pages 398-404, June.
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    Cited by:

    1. Yu-Min Huang, 2019. "Binary surrogates with stratified samples when weights are unknown," Computational Statistics, Springer, vol. 34(2), pages 653-682, June.

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