Localio A R, Berlin J A, Ten Have T R, Kimmel S E
Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, 606 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021, USA.
Ann Intern Med. 2001 Jul 17;135(2):112-23. doi: 10.7326/0003-4819-135-2-200107170-00012.
Increasingly, investigators rely on multicenter or multigroup studies to demonstrate effectiveness and generalizability. Authors too often overlook the analytic challenges in these study designs: the correlation of outcomes and exposures among patients within centers, confounding of associations by center, and effect modification of treatment or exposure across center. Correlation or clustering, resulting from the similarity of outcomes among patients within a center, requires an adjustment to confidence intervals and P values, especially in observational studies and in randomized multicenter studies in which treatment is allocated by center rather than by individual patient. Multicenter designs also warrant testing and adjustment for the potential bias of confounding by center, and for the presence of effect modification or interaction by center. This paper uses examples from the recent biomedical literature to highlight the issues and analytic options.
越来越多的研究者依靠多中心或多组研究来证明有效性和普遍性。作者常常忽视这些研究设计中的分析挑战:中心内患者的结局与暴露之间的相关性、中心对关联的混杂作用,以及跨中心的治疗或暴露效应修饰。中心内患者结局的相似性导致的相关性或聚类,需要对置信区间和P值进行调整,尤其是在观察性研究以及治疗按中心而非个体患者分配的随机多中心研究中。多中心设计还需要对中心混杂的潜在偏倚以及中心效应修饰或交互作用的存在进行检验和调整。本文通过近期生物医学文献中的例子来突出这些问题及分析选项。