Gail M H
Biometrics. 1981 Mar;37(1):67-78.
The proportional hazards model of Cox (1972, Journal of the Royal Statistical Society, Series B 34, 187--220), with a time-dependent covariate, is used to analyze serial cancer marker data. A particular advantage of this method is the case with which missing marker data are handled. Analysis of a real data set shows that high levels of the cancer marker carcinoembryonic antigen (CEA) are associated with increased risk of death in patients with resected colorectal cancer. Several aspects of CEA marker history are analyzed, including CEA level at death time t, CEA level 200 days prior to time t, and whether or not CEA exceeded 5 ng/ml prior to t. Methods to test the hypothesis of no marker effect and to give estimates and confidence intervals for model parameters are outlined both for continuous and for grouped time-to-response data. For grouped data a likelihood ratio test of the proportional hazards assumption is suggested.
采用带有时间依存性协变量的Cox比例风险模型（1972年，《皇家统计学会学报》，B辑第34卷，第187 - 220页）对系列癌症标志物数据进行分析。该方法的一个特别优势在于处理缺失标志物数据的情形。对一个实际数据集的分析表明，癌胚抗原（CEA）水平较高与接受过手术的结直肠癌患者死亡风险增加相关。分析了CEA标志物历史的几个方面，包括死亡时间t时的CEA水平、时间t前200天的CEA水平以及在t之前CEA是否超过5 ng/ml。针对连续型和分组的反应时间数据，概述了检验无标志物效应假设以及给出模型参数估计值和置信区间的方法。对于分组数据，建议进行比例风险假设的似然比检验。
