An empirical comparison of several clustered data approaches under confounding due to cluster effects in the analysis of complications of coronary angioplasty
An empirical comparison of several clustered data approaches under confounding due to cluster effects in the analysis of complications of coronary angioplasty
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DOI：
10.1111/j.0006-341x.1999.00470.x
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发表时间：
1999-06-01
期刊：
影响因子：
1.9
通讯作者：
Sammel, MD
中科院分区：
文献类型：
--
作者：
Berlin, JA;Kimmel, SE;Sammel, MD
在分析来自许多类型的大型研究的二进制响应数据时，数据可能来自多个中心，导致响应的中心内相关性。当中心内的结果往往比其他中心的结果更相似时，就会出现这种相关性或聚类。在研究中，各中心在关注的暴露方面也存在差异，即使在考虑了中心内相关性后，也可能混淆对确诊-结局相关性的分析。我们应用几种分析方法来比较与两种策略相关的主要并发症的风险，分期和联合手术，用于进行经皮腔内冠状动脉成形术（PTCA），一种缓解动脉粥样硬化所致血管阻塞的机械方法。联合手术在一些中心被用作削减成本的策略。我们进行了大量的群体平均和集群特定（条件）分析，其中（a）不对任何类型的中心效应进行调整;（B）仅对中心对响应的影响进行调整;或（c）对中心对响应的影响以及中心与暴露之间的关系进行调整。用于第三种方法的方法将手术类型变量分解为中心内和中心间分量，得到两个比值比估计值。忽略聚类的朴素分析给出了手术类型的高度显著影响（OR = 1.6）。群体平均模型给出的治疗类型OR估计值为1.6 - 1.2，仅针对中心对缓解的影响进行调整，范围为极轻微至非常不显著。这些结果依赖于假设的相关结构。将治疗类型变量分解为中心间和中心内组分的条件（集群特异性）模型和其他方法均未发现手术类型的中心内效应（OR = 1.02，一致）和相当大的中心内效应。结果的这种中心间变异性与接受联合手术的患者比例有关，即使在手术类型（中心内）和其他患者和中心水平协变量的条件下也发现了这种变异性。该示例说明了当平均暴露在不同群集之间变化时，解决中心效应混淆结果-暴露关联的可能性的重要性。虽然条件方法提供了集群内效应的估计值，但它们不提供关于多中心效应的信息。我们建议使用分解方法，因为它提供两种类型的估计。
In the analysis of binary response data from many types of large studies, the data are likely to have arisen from multiple centers, resulting in a within-center correlation for the response. Such correlation, or clustering, occurs when outcomes within centers tend to be more similar to each other than to outcomes in other centers. In studies where there is also variability among centers with respect to the exposure of interest, analysis of the exposure-outcome association may be confounded, even after accounting for within-center correlations. We apply several analytic methods to compare the risk of major complications associated with two strategies, staged and combined procedures, for performing percutaneous transluminal coronary angioplasty (PTCA), a mechanical means of relieving blockage of blood vessels due to atherosclerosis. Combined procedures are used in some centers as a cost-cutting strategy. We performed a number of population-averaged and cluster-specific (conditional) analyses, which (a) make no adjustments for center effects of any kind; (b) make adjustments for the effect of center on only the response; or (c) make adjustments for both the effect of center on the response and the relationship between center and exposure. The method used for this third approach decomposes the procedure type variable into within-center and among-center components, resulting in two odds ratio estimates. The naive analysis, ignoring clusters, gave a highly significant effect of procedure type (OR = 1.6). Population average models gave marginally to very nonsignificant estimates of the OR for treatment type ranging from 1.6 to 1.2 with adjustment only for the effect of centers on response. These results depended on the assumed correlation structure. Conditional (cluster-specific) models and other methods that decomposed the treatment type variable into among- and within-center components all found no within-center effect of procedure type (OR = 1.02, consistently) and a considerable among-center effect. This among-center variability in outcomes was related to the proportion of patients who receive combined procedures and was found even when conditioned on procedure type (within-center) and other patient- and center-level covariates. This example illustrates the importance of addressing the potential for center effects to confound an outcome-exposure association when average exposure varies across clusters. While conditional approaches provide estimates of the within-cluster effect, they do not provide information about among-center effects. We recommend using the decomposition approach, as it provides both types of estimates.