Statistical Methods For Clustered Data In Epidemiology

Environmental Health Sciences

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Abstract

Progess has been made in two areas this past year. (1) We have shown that random effect models for clustered data structure are preferable in that they will accommodate the correlation for individuals within a given cluster and also allow the results to be interpreted to a more broad underlying population. (2) We have also shown that when studying a continuous marker of health, such as blood pressure, one can markedly improve the efficiency of a study (over what would be achieved with random sampling) by using an outcome dependent sampling design, which oversamples observations at the extremes, i.e. people with unusually high or low values of the outcome. The analytic strategy is being further developed and applied to studies of neurodevelopmental scores in relation to pesticide exposure.