Department of Epidemiology and Biostatistics, University of Georgia, Athens, GA 30602, USA.
Stat Med. 2013 May 30;32(12):2013-30. doi: 10.1002/sim.5652. Epub 2012 Oct 9.
Missing covariates often arise in biomedical studies with survival outcomes. Existing approaches for missing covariates generally assume proportional hazards. The proportionality assumption may not hold in practice, as illustrated by data from a mouse leukemia study with covariate effects changing over time. To tackle this restriction, we study the missing data problem under the varying-coefficient proportional hazards model. On the basis of the local partial likelihood approach, we develop inverse selection probability weighted estimators. We consider reweighting and augmentation techniques for possible improvement of efficiency and robustness. The proposed estimators are assessed via simulation studies and illustrated by application to the mouse leukemia data.
缺失协变量在生存结局的生物医学研究中经常出现。现有的缺失协变量处理方法通常假设比例风险。但是，这种比例性假设在实际中可能并不成立，例如，来自一个随时间变化的协变量效应的小鼠白血病研究的数据说明了这一点。为了解决这个限制，我们在时变系数比例风险模型下研究缺失数据问题。基于局部部分似然方法，我们开发了逆选择概率加权估计量。我们考虑了重加权和扩充技术，以可能提高效率和稳健性。通过模拟研究评估了所提出的估计量，并通过应用于小鼠白血病数据来说明。
