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Model selection for semiparametric marginal mean regression accounting for within‐cluster subsampling variability and informative cluster size

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  • Chung‐Wei Shen
  • Yi‐Hau Chen

Abstract

We propose a model selection criterion for semiparametric marginal mean regression based on generalized estimating equations. The work is motivated by a longitudinal study on the physical frailty outcome in the elderly, where the cluster size, that is, the number of the observed outcomes in each subject, is “informative” in the sense that it is related to the frailty outcome itself. The new proposal, called Resampling Cluster Information Criterion (RCIC), is based on the resampling idea utilized in the within‐cluster resampling method (Hoffman, Sen, and Weinberg, 2001, Biometrika 88, 1121–1134) and accommodates informative cluster size. The implementation of RCIC, however, is free of performing actual resampling of the data and hence is computationally convenient. Compared with the existing model selection methods for marginal mean regression, the RCIC method incorporates an additional component accounting for variability of the model over within‐cluster subsampling, and leads to remarkable improvements in selecting the correct model, regardless of whether the cluster size is informative or not. Applying the RCIC method to the longitudinal frailty study, we identify being female, old age, low income and life satisfaction, and chronic health conditions as significant risk factors for physical frailty in the elderly.

Suggested Citation

  • Chung‐Wei Shen & Yi‐Hau Chen, 2018. "Model selection for semiparametric marginal mean regression accounting for within‐cluster subsampling variability and informative cluster size," Biometrics, The International Biometric Society, vol. 74(3), pages 934-943, September.
    • Handle: RePEc:bla:biomet:v:74:y:2018:i:3:p:934-943
    DOI: 10.1111/biom.12869
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    References listed on IDEAS

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    1. Eva Cantoni & Joanna Mills Flemming & Elvezio Ronchetti, 2005. "Variable Selection for Marginal Longitudinal Generalized Linear Models," Biometrics, The International Biometric Society, vol. 61(2), pages 507-514, June.
    2. Riccardo De Bin & Silke Janitza & Willi Sauerbrei & Anne-Laure Boulesteix, 2016. "Subsampling versus bootstrapping in resampling-based model selection for multivariable regression," Biometrics, The International Biometric Society, vol. 72(1), pages 272-280, March.
    3. Wei Pan, 2001. "Akaike's Information Criterion in Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 57(1), pages 120-125, March.
    4. Chung-Wei Shen & Yi-Hau Chen, 2012. "Model Selection for Generalized Estimating Equations Accommodating Dropout Missingness," Biometrics, The International Biometric Society, vol. 68(4), pages 1046-1054, December.
    5. Claeskens,Gerda & Hjort,Nils Lid, 2008. "Model Selection and Model Averaging," Cambridge Books, Cambridge University Press, number 9780521852258, October.
    6. John M. Williamson & Somnath Datta & Glen A. Satten, 2003. "Marginal Analyses of Clustered Data When Cluster Size Is Informative," Biometrics, The International Biometric Society, vol. 59(1), pages 36-42, March.
    7. Shaun R. Seaman & Menelaos Pavlou & Andrew J. Copas, 2014. "Methods for observed-cluster inference when cluster size is informative: A review and clarifications," Biometrics, The International Biometric Society, vol. 70(2), pages 449-456, June.
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