The manuscript discusses how to incorporate random effects for quantile regression models for clustered data with focus on settings with many but small clusters. The paper has three contributions: (i) documenting that existing methods may lead to severely biased estimators for fixed effects parameters; (ii) proposing a new two-step estimation methodology where predictions of the random effects are first computed {by a pseudo likelihood approach (the LQMM method)} and then used as offsets in standard quantile regression; (iii) proposing a novel bootstrap sampling procedure in order to reduce bias of the two-step estimator and compute confidence intervals. The proposed estimation and associated inference is assessed numerically through rigorous simulation studies and applied to an AIDS Clinical Trial Group (ACTG) study.

翻译：文稿讨论了如何将随机效应纳入集群数据四分位回归模型,重点是多组但小组的设置,论文有三项贡献:(一) 记录现有方法可能导致固定效应参数的严重偏差估计;(二) 提出一个新的两步估计方法,即随机效应预测首先以假可能性方法(LQMM方法)进行计算,然后用作标准四分位回归的抵消;(三) 提出新的靴套采样程序,以减少两步估测器的偏差,并计算信任间隔,拟议估算和相关推论通过严格的模拟研究进行数字评估,并应用于艾滋病临床试验组的研究。