Murray D M, Short B J
Department of Epidemiology, University of Minnesota, School of Public Health, Minneapolis 55454-1015, USA.
Addict Behav. 1997 Jan-Feb;22(1):1-12. doi: 10.1016/0306-4603(95)00099-2.
Tobacco intervention studies that employ a community trial design require adjustment to the usual analytic methods to account for the allocation of intact social groups to study conditions and the positive intraclass correlation (p) that is inevitable in such a design. In the absence of valid estimates of the relevant p, investigators seeking to establish an appropriate sample size could only guess about the magnitude of the problem. We recently published estimates of p for common measures of adolescent tobacco use, but those estimates were unadjusted for potential covariates and so represented an upper limit on the magnitude of p. This report demonstrates how estimates of intraclass correlation may be substantially reduced through regression adjustment for easily measured covariates. Results show that both the p and the residual variance can be reduced, by an average of 20 and 11%, respectively, offering greater efficiency for investigators who plan future studies and who are able to measure those covariates in their studies. Future work should seek both to replicate this work and to extend it; for example, to cohort designs where the improvements might be even greater.
采用社区试验设计的烟草干预研究需要对常用分析方法进行调整，以考虑完整社会群体被分配到研究条件中的情况以及这种设计中不可避免的组内正相关（p）。在缺乏相关p的有效估计值的情况下，试图确定合适样本量的研究者只能猜测该问题的严重程度。我们最近发表了青少年烟草使用常见测量指标的p估计值，但这些估计值未针对潜在协变量进行调整，因此代表了p大小的上限。本报告展示了如何通过对易于测量的协变量进行回归调整，大幅降低组内相关估计值。结果表明，p和残差方差平均分别可降低20%和11%，这为计划未来研究且能够在研究中测量这些协变量的研究者提供了更高的效率。未来的工作应既寻求重复此项工作，也寻求对其进行扩展；例如，扩展到可能有更大改进的队列设计。
