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On the robustness of weighted methods for fitting models to case–control data

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  • Alastair Scott
  • Chris Wild

Abstract

Summary. We compare the robustness under model misspecification of two approaches to fitting logistic regression models with unmatched case–control data. One is the standard survey approach based on weighted versions of population estimating equations. The other is the likelihood‐based approach that is standard in medical applications. The conventional view is that the (less efficient) survey‐weighted approach leads to greater robustness. We conclude that this view is not always justified.

Suggested Citation

  • Alastair Scott & Chris Wild, 2002. "On the robustness of weighted methods for fitting models to case–control data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 207-219, May.
    • Handle: RePEc:bla:jorssb:v:64:y:2002:i:2:p:207-219
    DOI: 10.1111/1467-9868.00333
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    References listed on IDEAS

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    1. Manski, Charles F. & Thompson, T. Scott, 1989. "Estimation of best predictors of binary response," Journal of Econometrics, Elsevier, vol. 40(1), pages 97-123, January.
    2. Yu Xie & Charles F. Manski, 1989. "The Logit Model and Response-Based Samples," Sociological Methods & Research, , vol. 17(3), pages 283-302, February.
    3. J. F. Lawless & J. D. Kalbfleisch & C. J. Wild, 1999. "Semiparametric methods for response‐selective and missing data problems in regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 413-438, April.
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    Cited by:

    1. Robert B. Nielsen & Martin C. Seay, 2014. "Complex Samples and Regression-Based Inference: Considerations for Consumer Researchers," Journal of Consumer Affairs, Wiley Blackwell, vol. 48(3), pages 603-619, October.
    2. Skinner, Chris J., 2018. "Analysis of categorical data for complex surveys," LSE Research Online Documents on Economics 89707, London School of Economics and Political Science, LSE Library.
    3. Alan Lee & Yuichi Hirose, 2010. "Semi-parametric efficiency bounds for regression models under response-selective sampling: the profile likelihood approach," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(6), pages 1023-1052, December.
    4. Yanyuan Ma & Raymond J. Carroll, 2016. "Semiparametric estimation in the secondary analysis of case–control studies," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 127-151, January.

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