Liu Minzhao, Daniels Michael J, Perri Michael G
Department of Statistics, University of Florida, FL 32601, USA.
Department of Integrative Biology, Department of Statistics & Data Sciences, The University of Texas at Austin, 141MC Patterson Hall, Austin, TX 78712, USA
Biostatistics. 2016 Jan;17(1):108-21. doi: 10.1093/biostatistics/kxv023. Epub 2015 Jun 3.
In this paper, we develop methods for longitudinal quantile regression when there is monotone missingness. In particular, we propose pattern mixture models with a constraint that provides a straightforward interpretation of the marginal quantile regression parameters. Our approach allows sensitivity analysis which is an essential component in inference for incomplete data. To facilitate computation of the likelihood, we propose a novel way to obtain analytic forms for the required integrals. We conduct simulations to examine the robustness of our approach to modeling assumptions and compare its performance to competing approaches. The model is applied to data from a recent clinical trial on weight management.
在本文中，我们开发了在存在单调缺失值情况下的纵向分位数回归方法。特别地，我们提出了具有一种约束的模式混合模型，该约束为边际分位数回归参数提供了直接的解释。我们的方法允许进行敏感性分析，这是对不完整数据进行推断的一个重要组成部分。为便于似然性的计算，我们提出了一种新颖的方法来获得所需积分的解析形式。我们进行模拟以检验我们的方法对建模假设的稳健性，并将其性能与竞争方法进行比较。该模型应用于最近一项关于体重管理的临床试验数据。
