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首页> 外文期刊>Statistical methods in medical research >Bayesian nonparametric mixed-effects joint model for longitudinal-competing risks data analysis in presence of multiple data features
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Bayesian nonparametric mixed-effects joint model for longitudinal-competing risks data analysis in presence of multiple data features

机译:贝叶斯非参数混合效应联合模型,用于纵向竞争风险的数据分析在存在多种数据特征

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>Recently, the joint analysis of longitudinal and survival data has been an active research area. Most joint models focus on survival data with only one type of failure. The research on joint modeling of longitudinal and competing risks survival data is sparse. Even so, many joint models for this type of data assume parametric function forms for both longitudinal and survival sub-models, thus limits their use. Further, the common data features that are usually observed in practice, such as asymmetric distribution and missingness in response, measurement errors in covariate, need to be taken into account for reliable parameter estimation. The statistical inference is complicated when all these factors are considered simultaneously. In the article, driven by a motivating example, we assume nonparametric function forms for the varying coefficients in both longitudinal and competing risks survival sub-models. We propose a Bayesian nonparametric mixed-effects joint model for the analysis of longitudinal-competing risks data with asymmetry, missingness, and measurement errors. Simulation studies are conducted to assess the performance of the proposed method. We apply the proposed method to an AIDS dataset and compare a few candidate models under various settings. Some interesting results are reported.
机译:>最近,纵向和生存数据的联合分析是一个活跃的研究区。大多数联合模型专注于生存数据,只有一种失败。纵向和竞争风险联合建模的研究生存数据是稀疏的。即便如此,这种类型的数据的联合模型也呈现用于纵向和生存子模型的参数函数形式,从而限制了它们的使用。此外,在实践中通常观察到的公共数据特征,例如在响应中的不对称分布和缺失,需要考虑协变量的测量误差,以获得可靠的参数估计。当同时考虑所有这些因素时,统计推断是复杂的。在由动机示例驱动的文章中,我们假设纵向和竞争风险生存子模型中的不同系数的非参数函数形式。我们提出了一种贝叶斯非参数混合效应联合模型,用于分析纵向竞争风险数据,不对称,缺失和测量误差。进行仿真研究以评估所提出的方法的性能。我们将建议的方法应用于AIDS数据集,并在各种设置下比较一些候选模型。报告了一些有趣的结果。

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