Deng Yu, Zeng Donglin, Zhao Jinying, Cai Jianwen
Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, U.S.A.
Department of Epidemiology, Tulane University, New Orleans, Louisiana, U.S.A.
Biometrics. 2017 Sep;73(3):835-845. doi: 10.1111/biom.12655. Epub 2017 Mar 3.
In many epidemiology studies, family data with survival endpoints are collected to investigate the association between risk factors and disease incidence. Sometimes the risk of the disease may change when a certain risk factor exceeds a certain threshold. Finding this threshold value could be important for disease risk prediction and diseases prevention. In this work, we propose a change-point proportional hazards model for clustered event data. The model incorporates the unknown threshold of a continuous variable as a change point in the regression. The marginal pseudo-partial likelihood functions are maximized for estimating the regression coefficients and the unknown change point. We develop a supremum test based on robust score statistics to test the existence of the change point. The inference for the change point is based on the m out of n bootstrap. We establish the consistency and asymptotic distributions of the proposed estimators. The finite-sample performance of the proposed method is demonstrated via extensive simulation studies. Finally, the Strong Heart Family Study dataset is analyzed to illustrate the methods.
在许多流行病学研究中，会收集带有生存终点的家庭数据，以调查风险因素与疾病发病率之间的关联。有时，当某个风险因素超过某个阈值时，疾病风险可能会发生变化。找到这个阈值对于疾病风险预测和疾病预防可能很重要。在这项工作中，我们针对聚类事件数据提出了一种变点比例风险模型。该模型将连续变量的未知阈值纳入回归中的一个变点。通过最大化边际伪偏似然函数来估计回归系数和未知变点。我们基于稳健得分统计量开发了一个上确界检验来检验变点是否存在。对变点的推断基于n中取m的自助法。我们建立了所提出估计量的一致性和渐近分布。通过广泛的模拟研究证明了所提方法的有限样本性能。最后，对强心脏家庭研究数据集进行分析以说明这些方法。
