Centre of Excellence for Nutrition, Faculty of Health Sciences, North-West University, Potchefstroom 2520, South Africa.
SAMRC Extramural Unit for Hypertension and Cardiovascular Disease, Faculty of Health Sciences, North-West University, Potchefstroom 2520, South Africa.
Int J Environ Res Public Health. 2023 Jul 20;20(14):6417. doi: 10.3390/ijerph20146417.
When the Cox model is applied, some recommendations about the choice of the time metric and the model's structure are often disregarded along with the proportionality of risk assumption. Moreover, most of the published studies fail to frame the real impact of a risk factor in the target population. Our aim was to show how modelling strategies affected Cox model assumptions. Furthermore, we showed how the Cox modelling strategies affected the population attributable risk (PAR). Our work is based on data collected in the North-West Province, one of the two PURE study centres in South Africa. The Cox model was used to estimate the hazard ratio (HR) of mortality for all causes in relation to smoking, alcohol use, physical inactivity, and hypertension. Firstly, we used a Cox model with time to event as the underlying time variable. Secondly, we used a Cox model with age to event as the underlying time variable. Finally, the second model was implemented with age classes and sex as strata variables. Mutually adjusted models were also investigated. A statistical test to the multiplicative interaction term the exposures and the log transformed time to event metric was used to assess the proportionality of risk assumption. The model's fitting was investigated by means of the Akaike Information Criteria (AIC). Models with age as the underlying time variable with age and sex as strata variables had enhanced validity of the risk proportionality assumption and better fitting. The PAR for a specific modifiable risk factor can be defined more accurately in mutually adjusted models allowing better public health decisions. This is not necessarily true when correlated modifiable risk factors are considered.
当 Cox 模型被应用时，通常会忽略关于时间度量和模型结构的选择以及风险假设的比例性的一些建议。此外，大多数已发表的研究未能在目标人群中阐明风险因素的实际影响。我们的目的是展示建模策略如何影响 Cox 模型假设。此外，我们还展示了 Cox 建模策略如何影响人群归因风险 (PAR)。我们的工作基于在南非两个 PURE 研究中心之一的西北省收集的数据。Cox 模型用于估计与吸烟、饮酒、身体活动不足和高血压有关的所有原因死亡率的风险比 (HR)。首先，我们使用 Cox 模型，以事件时间作为基础时间变量。其次，我们使用 Cox 模型，以年龄到事件作为基础时间变量。最后，第二个模型使用年龄类别和性别作为分层变量来实现。还研究了相互调整的模型。使用暴露和对数转换的事件时间度量的乘积交互项的统计检验来评估风险假设的比例性。通过赤池信息量准则 (AIC) 来研究模型的拟合情况。以年龄作为基础时间变量的模型，以及将年龄和性别作为分层变量的模型，增强了风险比例假设的有效性和更好的拟合度。在相互调整的模型中，可以更准确地定义特定可修改风险因素的 PAR，从而可以做出更好的公共卫生决策。当考虑相关的可修改风险因素时，情况不一定如此。
