A comparison of time to event analysis methods, using weight status and breast cancer as a case study.
A comparison of time to event analysis methods, using weight status and breast cancer as a case study.
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时间事件分析方法的比较，以体重状况和乳腺癌为例研究。
DOI：
10.1038/s41598-021-92944-z
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发表时间：
2021-07-07
影响因子：
4.6
通讯作者：
Morris MA
中科院分区：
文献类型：
--
作者：
Aivaliotis G;Palczewski J;Atkinson R;Cade JE;Morris MA
传统上使用考克斯比例风险模型对队列研究数据进行生存分析。随机生存森林（RSFs）是一种机器学习方法，现在提出了一种替代方法。使用英国妇女队列研究（n = 34，493），我们评估了两种方法：考克斯模型和RSF，以调查身体质量指数和乳腺癌发病率之间的关系。通过交叉验证和Bootstrap评估模型的稳健性。报告了自举系数的直方图。报告所有型号的C指数和综合Brier评分。在绝经后妇女中，超重（OW）和肥胖（O）的考克斯模型风险比（HR）分别为1.25（1.04，1.51）和1.28（0.98，1.68），OW和O的RSF比值比（OR）部分依赖于绝经，分别为1.34（1.31，1.70）和1.45（1.42，1.48）。HR为非显著性结果。只有RSF似乎对体重状态对事件发生时间的影响有信心。自举法表明，考克斯模型系数可能会有很大的变化，削弱解释的潜力。RSF用于产生部分依赖图（PDP），显示OW和O体重状态增加绝经后妇女乳腺癌发病率的概率。所有型号都具有相对较低的C指数和较高的综合Brier评分。RSF过度拟合数据。在我们的研究中，RSF可以识别数据中复杂的非比例风险类型模式，并允许使用PDP研究更复杂的关系，但它过度拟合限制了对新实例的结果外推。此外，它比考克斯模型更不容易解释。生存分析的价值仍然是至关重要的，因此像RSF这样的机器学习技术应该被视为另一种分析方法。
Survival analysis with cohort study data has been traditionally performed using Cox proportional hazards models. Random survival forests (RSFs), a machine learning method, now present an alternative method. Using the UK Women’s Cohort Study (n = 34,493) we evaluate two methods: a Cox model and an RSF, to investigate the association between Body Mass Index and time to breast cancer incidence. Robustness of the models were assessed by cross validation and bootstraping. Histograms of bootstrap coefficients are reported. C-Indices and Integrated Brier Scores are reported for all models. In post-menopausal women, the Cox model Hazard Ratios (HR) for Overweight (OW) and Obese (O) were 1.25 (1.04, 1.51) and 1.28 (0.98, 1.68) respectively and the RSF Odds Ratios (OR) with partial dependence on menopause for OW and O were 1.34 (1.31, 1.70) and 1.45 (1.42, 1.48). HR are non-significant results. Only the RSF appears confident about the effect of weight status on time to event. Bootstrapping demonstrated Cox model coefficients can vary significantly, weakening interpretation potential. An RSF was used to produce partial dependence plots (PDPs) showing OW and O weight status increase the probability of breast cancer incidence in post-menopausal women. All models have relatively low C-Index and high Integrated Brier Score. The RSF overfits the data. In our study, RSF can identify complex non-proportional hazard type patterns in the data, and allow more complicated relationships to be investigated using PDPs, but it overfits limiting extrapolation of results to new instances. Moreover, it is less easily interpreted than Cox models. The value of survival analysis remains paramount and therefore machine learning techniques like RSF should be considered as another method for analysis.