Statistical Methods For Clustered Data In Epidemiology

去年在两个领域取得了进展。(1)我们已经证明，聚类数据结构的随机效应模型是更可取的，因为它们将适应给定聚类中个体的相关性，并且还允许将结果解释为更广泛的底层人群。(2)我们还表明，当研究一个连续的健康标志物时，如血压，人们可以通过使用结果依赖的抽样设计来显著提高研究的效率（与随机抽样相比），该设计在极端情况下对观察结果进行过采样，即具有异常高或低值的人。分析策略正在进一步发展和应用于研究神经发育分数与农药暴露。

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.