Abstract
In order to measure the association between an exposure variable X and an outcome variable Y , we introduce the effect-control sampling design and we consider the family of symmetric association measures. Focusing on the case of binary exposure and outcome variables, a general estimator of such measures is proposed and its asymptotic properties are also discussed. We define an allocation procedure for a stratified effect-control design, which is optimal in terms of the variance of such an estimator. Finally, small sample behavior is investigated by Monte Carlo simulation for a measure belonging to the family, which we believe particularly interesting as it possess the appealing property of being normalized.
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Borgoni, R., Marasini, D., Quatto, P. (2013). Symmetric Association Measures in Effect-Control Sampling. In: Torelli, N., Pesarin, F., Bar-Hen, A. (eds) Advances in Theoretical and Applied Statistics. Studies in Theoretical and Applied Statistics(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35588-2_24
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DOI: https://doi.org/10.1007/978-3-642-35588-2_24
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