School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, China.
School of Mathematics and Statistics, Central China Normal University, Hubei, China.
Stat Methods Med Res. 2021 Sep;30(9):2017-2031. doi: 10.1177/09622802211023957. Epub 2021 Jul 15.
In HIV vaccine efficacy trials, mark-specific hazards models have important applications and can be used to evaluate the strain-specific vaccine efficacy. Additive hazards models have been widely used in practice, especially when continuous covariates are present. In this article, we conduct variable selection for a mark-specific additive hazards model. The proposed method is based on an estimating equation with the first derivative of the adaptive LASSO penalty function. The asymptotic properties of the resulting estimators are established. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a dataset from the first HIV vaccine efficacy trial is provided.
在 HIV 疫苗功效试验中，标记特异性危害模型具有重要的应用，可以用于评估针对特定毒株的疫苗功效。加性危害模型在实践中得到了广泛应用，尤其是在存在连续协变量的情况下。本文针对标记特异性加性危害模型进行变量选择。所提出的方法基于具有自适应 LASSO 惩罚函数一阶导数的估计方程。建立了所得估计量的渐近性质。通过模拟研究评估了所提出的估计量的有限样本性能，并提供了对首个 HIV 疫苗功效试验数据集的应用。
