Semiparametric regression models and sensitivity analysis of longitudinal data with nonrandom dropouts.
Semiparametric regression models and sensitivity analysis of longitudinal data with nonrandom dropouts.
复制标题
非随机丢失纵向数据的半参数回归模型和敏感性分析。
DOI：
10.1111/j.1467-9574.2009.00435.x
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发表时间：
2010
影响因子：
1.5
通讯作者：
Peng,Limin
中科院分区：
文献类型：
--
作者：
Todem,David;Kim,Kyungmann;Fine,Jason;Peng,Limin
我们提出了一系列回归模型，用于在具有充分观察协变量的纵向结局分析中调整非随机脱落。该方法在概念上侧重于具有随机效应的广义线性模型。一种新的配方的共享随机效应模型，并提供了一个有意义的解释辍学选择参数。建议的半参数和参数模型的敏感性分析的一部分，以描绘与观察到的数据一致的推断范围。通过固定一些模型参数来构造函数估计量，这些函数估计量被用作参数对比的全局敏感性检验的基础，从而解决了对模型可识别性的担忧。我们的模拟研究表明，在脱落率较高或脱落模型指定错误的情况下，半参数模型相对于参数模型的偏倚大幅降低。该方法的实际效用说明在数据分析。
We propose a family of regression models to adjust for non‐random dropouts in the analysis of longitudinal outcomes with fully observed covariates. The approach conceptually focuses on generalized linear models with random effects. A novel formulation of a shared random effects model is presented and shown to provide a dropout selection parameter with a meaningful interpretation. The proposed semiparametric and parametric models are made part of a sensitivity analysis to delineate the range of inferences consistent with observed data. Concerns about model identifiability are addressed by fixing some model parameters to construct functional estimators that are used as the basis of a global sensitivity test for parameter contrasts. Our simulation studies demonstrate a large reduction of bias for the semiparametric model relative to the parametric model at times where the dropout rate is high or the dropout model is mis‐specified. The methodology's practical utility is illustrated in a data analysis.