Greenland S
Department of Epidemiology, UCLA School of Public Health, and Department of Statistics, UCLA College of Letters and Science, 22333 Swenson Drive, Topanga, CA 90290, USA.
Int J Epidemiol. 2001 Dec;30(6):1343-50. doi: 10.1093/ije/30.6.1343.
A number of authors have attempted to defend ecologic (aggregate) studies by claiming that the goal of those studies is estimation of ecologic (contextual or group-level) effects rather than individual-level effects. Critics of these attempts point out that ecologic effect estimates are inevitably used as estimates of individual effects, despite disclaimers. A more subtle problem is that ecologic variation in the distribution of individual effects can bias ecologic estimates of contextual effects. The conditions leading to this bias are plausible and perhaps even common in studies of ecosocial factors and health outcomes because social context is not randomized across typical analysis units (administrative regions). By definition, ecologic data contain only marginal observations on the joint distribution of individually defined confounders and outcomes, and so identify neither contextual nor individual-level effects. While ecologic studies can still be useful given appropriate caveats, their problems are better addressed by multilevel study designs, which obtain and use individual as well as group-level data. Nonetheless, such studies often share certain special problems with ecologic studies, including problems due to inappropriate aggregation and problems due to temporal changes in covariate distributions.
许多作者试图为生态(总体)研究辩护,声称这些研究的目标是估计生态(背景或群体层面)效应,而非个体层面效应。对这些尝试的批评者指出,尽管有免责声明,但生态效应估计不可避免地被用作个体效应的估计。一个更微妙的问题是,个体效应分布中的生态变异可能会使背景效应的生态估计产生偏差。导致这种偏差的条件似乎合理,甚至在生态社会因素与健康结果的研究中可能很常见,因为社会背景并非在典型分析单位(行政区)之间随机分布。根据定义,生态数据仅包含关于个体定义的混杂因素和结果联合分布的边际观察值,因此既无法识别背景效应,也无法识别个体层面效应。虽然在给出适当警告的情况下,生态研究仍然可能有用,但通过多层次研究设计能更好地解决它们的问题,多层次研究设计会获取并使用个体以及群体层面的数据。尽管如此,此类研究通常与生态研究存在某些特殊问题,包括因不适当汇总导致的问题以及因协变量分布随时间变化而产生的问题。