Competing risks analysis of correlated failure time data
Competing risks analysis of correlated failure time data
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DOI：
10.1111/j.1541-0420.2007.00868.x
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发表时间：
2008-03-01
期刊：
影响因子：
1.9
通讯作者：
Rosenberg, Philip S.
中科院分区：
文献类型：
--
作者：
Chein, Bingshu E.;Kramer, Joan L.;Rosenberg, Philip S.
当单个事件时间在集群内相关时，我们开发了竞争风险分析方法。聚类在临床遗传学研究和其他环境中自然出现。我们开发了累积发生率的非参数估计器，并获得了解释簇内相关性的稳健的逐点标准误差。我们修改了相关竞争风险数据的双样本格雷检验和佩佩-莫里检验，并提出了一个简单的双样本检验来衡量里程碑时间累积发生率的差异。在模拟研究中，我们的估计量是渐近无偏的，并且修改后的检验统计量控制了 I 类误差。各个双样本检验的功效对相关程度的敏感程度不同；最佳检验取决于感兴趣的替代假设和簇内相关性。出于说明目的，我们将我们的方法应用于遗传性乳腺癌/卵巢癌家族的基于家族的前瞻性队列研究。对于携带 BRCA1 突变的女性，我们估计了在卵巢癌死亡率竞争的情况下乳腺癌的累积发病率，并解释了显着的家庭内相关性。
We develop methods for competing risks analysis when individual event times are correlated within clusters. Clustering arises naturally in clinical genetic studies and other settings. We develop a nonparametric estimator of cumulative incidence, and obtain robust pointwise standard errors that account for within-cluster correlation. We modify the two-sample Gray and Pepe-Mori tests for correlated competing risks data, and propose a simple two-sample test of the difference in cumulative incidence at a landmark time. In simulation studies, our estimators are asymptotically unbiased, and the modified test statistics control the type I error. The power of the respective two-sample tests is differentially sensitive to the degree of correlation; the optimal test depends on the alternative hypothesis of interest and the within-cluster correlation. For purposes of illustration, we apply our methods to a family-based prospective cohort study of hereditary breast/ovarian cancer families. For women with BRCA1 mutations, we estimate the cumulative incidence of breast cancer in the presence of competing mortality from ovarian cancer, accounting for significant within-family correlation.