We propose a family of regression models to adjust for nonrandom 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 relatively to the parametric model at times where the dropout rate is high or the dropout model is misspecified. The methodology's practical utility is illustrated in a data analysis.

中文翻译：

---

具有非随机丢失的纵向数据的半参数回归模型和敏感性分析

我们提出了一系列回归模型，以在具有完全观察到的协变量的纵向结果分析中调整非随机辍学。该方法在概念上侧重于具有随机效应的广义线性模型。提出并展示了共享随机效应模型的新公式，以提供具有有意义解释的 dropout 选择参数。提议的半参数和参数模型是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建函数估计器，这些函数估计器用作参数对比的全局敏感性测试的基础，从而解决了对模型可识别性的担忧。我们的模拟研究表明，在丢失率高或丢失模型指定错误的情况下，半参数模型相对于参数模型的偏差大大减少。数据分析说明了该方法的实际效用。