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Abstract:
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In matched case-crossover studies, any stratum effect is removed by the conditioning on the fixed number of sets of the case and controls in the stratum. Therefore the conditional logistic regression model is not able to detect any effects associated with matching covariates. However, some matching variables such as time and spatial location often modify the effect of covariates, making the estimations obtained by conditional logistic regression incorrect. Hence, we propose a semiparametric time varying coefficient model, as well as two spatio-temporal varying coefficient models to evaluate these effects in order to make correct statistical inference. Our proposed models are developed under the Bayesian hierarchical model framework, and allow us to simultaneously detect relationships between the predictor and binary outcomes, between the predictor and time, and determine whether there is an effect modification due to spatial location. We demonstrate the accuracy of the estimation using a simulation study and an epidemiological example of a 1-4 bi-directional case-crossover study of childhood aseptic meningitis with drinking water turbidity.
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