Analysis of clustered binary outcomes using within-cluster paired resampling
Analysis of clustered binary outcomes using within-cluster paired resampling
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DOI：
10.1111/j.0006-341x.2002.00332.x
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发表时间：
2002-06-01
期刊：
影响因子：
1.9
通讯作者：
Weinberg, CR
中科院分区：
文献类型：
--
作者：
Rieger, RH;Weinberg, CR
条件逻辑回归（CSTR）是有用的，用于分析集群的二进制结果数据时，兴趣在于估计集群特定的暴露参数，同时治疗的依赖性所产生的随机集群效应作为滋扰。将未测量的集群特定因素聚合为集群特定基线风险，并且在存在未建模的异质协变量效应或集群内依赖性的情况下无效。我们提出了一种替代的，基于重采样的方法，用于分析聚类的二进制结果数据，内集群配对resolution（WCPR），它允许内集群的依赖性，而不仅仅是由于基线异质性。例如，依赖性可能部分由响应于由于未测量的辅因子而跨集群暴露的异质性引起。当两种方法都有效时，我们的模拟表明，这两种方法都可以执行自适应。当浪涌失效时，WCPR继续具有良好的运行特性。为了说明，我们应用WCPR和牙周数据集，其中有异质性，在跨集群的曝光。
Conditional logistic regression (CLR) is useful for analyzing clustered binary outcome data when interest lies in estimating a cluster-specific exposure parameter while treating the dependency arising from random cluster effects as a nuisance. CLR aggregates unmeasured clustor-specific factors into a cluster-specific baseline risk and is invalid in the presence of unmodeled heterogeneous covariate effects or within-cluster dependency. We propose an alternative, resampling-based method for analyzing clustered binary outcome data, within-cluster paired resampling (WCPR), which allows for within-cluster dependency not solely due to baseline heterogeneity. For example, dependency may be in part caused by heterogeneity in response to an exposure across clusters due to unmeasured cofactors. When both CLR and WCPR are valid, our simulations suggest that the two methods perform comparably. When CLR is invalid, WCPR continues to have good operating characteristics. For illustration, we apply both WCPR and CLR to a periodontal data set where there is heterogeneity in response to exposure across clusters.