Jones Edmund, Sweeting Michael J, Sharp Stephen J, Thompson Simon G
Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Worts' Causeway, Cambridge, CB1 8RN, UK.
Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Worts' Causeway, Cambridge, CB1 8RN, UK.
J Clin Epidemiol. 2015 Dec;68(12):1397-405. doi: 10.1016/j.jclinepi.2015.04.007. Epub 2015 Apr 30.
A case-cohort study is an efficient epidemiological study design for estimating exposure-outcome associations. When sampling of the subcohort is stratified, several methods of analysis are possible, but it is unclear how they compare. Our objective was to compare five analysis methods using Cox regression for this type of data, ranging from a crude model that ignores the stratification to a flexible one that allows nonproportional hazards and varying covariate effects across the strata.
We applied the five methods to estimate the association between physical activity and incident type 2 diabetes using data from a stratified case-cohort study and also used artificial data sets to exemplify circumstances in which they can give different results.
In the diabetes study, all methods except the method that ignores the stratification gave similar results for the hazard ratio associated with physical activity. In the artificial data sets, the more flexible methods were shown to be necessary when certain assumptions of the simpler models failed. The most flexible method gave reliable results for all the artificial data sets.
The most flexible method is computationally straightforward, and appropriate whether or not key assumptions made by the simpler models are valid.
病例队列研究是一种用于估计暴露-结局关联的高效流行病学研究设计。当亚队列的抽样进行分层时，有几种分析方法可供选择，但它们之间的比较尚不清楚。我们的目的是使用Cox回归比较五种针对此类数据的分析方法，范围从忽略分层的粗略模型到允许非比例风险以及各层间协变量效应不同的灵活模型。
我们应用这五种方法，利用分层病例队列研究的数据估计体力活动与2型糖尿病发病之间的关联，还使用人工数据集来说明它们可能得出不同结果的情况。
在糖尿病研究中，除了忽略分层的方法外，所有方法得出的与体力活动相关的风险比结果相似。在人工数据集中，当较简单模型的某些假设不成立时，显示更灵活的方法是必要的。最灵活的方法对所有人工数据集都给出了可靠的结果。
最灵活的方法计算简单，无论较简单模型所做的关键假设是否有效都适用。
