Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada, N2L 3G1.
Stat Med. 2013 Mar 15;32(6):1004-15. doi: 10.1002/sim.5581. Epub 2012 Aug 28.
Studies of chronic diseases routinely sample individuals subject to conditions on an event time of interest. In epidemiology, for example, prevalent cohort studies aiming to evaluate risk factors for survival following onset of dementia require subjects to have survived to the point of screening. In clinical trials designed to assess the effect of experimental cancer treatments on survival, patients are required to survive from the time of cancer diagnosis to recruitment. Such conditions yield samples featuring left-truncated event time distributions. Incomplete covariate data often arise in such settings, but standard methods do not deal with the fact that individuals' covariate distributions are also affected by left truncation. We describe an expectation-maximization algorithm for dealing with incomplete covariate data in such settings, which uses the covariate distribution conditional on the selection criterion. We describe an extension to deal with subgroup analyses in clinical trials for the case in which the stratification variable is incompletely observed.
慢性病研究通常会对处于感兴趣的事件时间的个体进行抽样。例如，在流行病学中，旨在评估痴呆发病后生存风险因素的现患队列研究要求受试者必须存活到筛选阶段。在旨在评估实验性癌症治疗对生存影响的临床试验中，患者必须从癌症诊断到招募时存活。这些条件产生了具有左截断事件时间分布的样本。在这种情况下，经常会出现不完全的协变量数据，但标准方法并没有考虑到个体的协变量分布也受到左截断的影响。我们描述了一种在这种情况下处理不完全协变量数据的期望最大化算法，该算法使用基于选择标准的协变量分布。我们还描述了一种扩展，用于处理临床试验中的亚组分析，其中分层变量未完全观察到的情况。
