Mean estimating equation approach to analysing cluster-correlated data with nonignorable cluster sizes
Mean estimating equation approach to analysing cluster-correlated data with nonignorable cluster sizes
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DOI：
10.1093/biomet/92.2.435
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发表时间：
2005-06-01
期刊：
影响因子：
2.7
通讯作者：
Scott, AJ
中科院分区：
文献类型：
--
作者：
Benhin, E;Rao, JNK;Scott, AJ
大多数用于分析聚类相关生物数据的方法都隐含地假设聚类大小的不确定性。当这个假设失败时，所得到的推论可能是渐进无效的。霍夫曼等人（2001年）提出了一种简单但计算密集的方法，该方法基于大量的聚类内重新采样和相关的单独估计方程，无论聚类大小是否可比较，都可以得出渐近有效的推断。我们研究了一个简单的方法，基于一个单一的逆簇大小加权估计方程，避免了resstrom，但导致渐近有效的推论。仿真结果评估所提出的方法的性能。我们还提出了Wald测试集群大小的可扩展性。
Most methods for analysing cluster-correlated biological data implicitly assume the ignorability of cluster sizes. When this assumption fails, the resulting inferences may be asymptotically invalid. Hoffman et al. (2001) proposed a simple but computationally intensive method, based on a large number of within-cluster resamples and associated separate estimating equations, that leads to asymptotically valid inferences whether the cluster sizes are ignorable or not. We study a simple method, based on a single inverse cluster size-weighted estimating equation, that avoids resampling and yet leads to asymptotically valid inferences. Simulation results are presented to assess the performance of the proposed method. We also propose Wald tests for ignorability of cluster sizes.