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Theory & Methods: Bias due to Ignoring the Sample Design in Case–Control Studies

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  • John M. Neuhaus

Abstract

Case–control studies allow efficient estimation of the associations of covariates with a binary response in settings where the probability of a positive response is small. It is well known that covariate–response associations can be consistently estimated using a logistic model by acting as if the case–control (retrospective) data were prospective, and that this result does not hold for other binary regression models. However, in practice an investigator may be interested in fitting a non–logistic link binary regression model and this paper examines the magnitude of the bias resulting from ignoring the case–control sample design with such models. The paper presents an approximation to the magnitude of this bias in terms of the sampling rates of cases and controls, as well as simulation results that show that the bias can be substantial.

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  • John M. Neuhaus, 2002. "Theory & Methods: Bias due to Ignoring the Sample Design in Case–Control Studies," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 44(3), pages 285-293, September.
    • Handle: RePEc:bla:anzsta:v:44:y:2002:i:3:p:285-293
    DOI: 10.1111/1467-842X.00231
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    Cited by:

    1. Mukherjee, Bhramar & Liu, Ivy, 2009. "A note on bias due to fitting prospective multivariate generalized linear models to categorical outcomes ignoring retrospective sampling schemes," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 459-472, March.

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