From the Guidelines
Cox regression is a statistical method used to analyze survival data, estimating the hazard ratio and investigating the relationship between survival time and predictor variables, as employed in a recent study on tobacco smoking cessation published in the International Journal of Epidemiology in 2022 1.
Key Characteristics of Cox Regression
- It handles censored data, where the outcome hasn't occurred for all subjects during the observation period.
- The model assumes that the hazard ratios remain constant over time (the proportional hazards assumption), as seen in the analysis of mortality outcomes in each cohort using Cox regression analysis to estimate hazard ratios (HRs) for mortality outcomes 1.
- Cox regression doesn't make assumptions about the shape of the baseline hazard function, making it semi-parametric and more flexible than fully parametric survival models.
- Researchers commonly use this technique in medical research to identify risk factors associated with survival outcomes while adjusting for multiple variables simultaneously, such as baseline age, education, marital status, residence area, and body mass index (BMI) 1.
Interpreting Cox Regression Results
- A hazard ratio greater than 1 indicates increased risk, while a value less than 1 suggests a protective effect of the variable being studied.
- The time course and magnitude of reduction in mortality can be estimated in former smokers relative to current smokers using Cox models, as demonstrated in the study on tobacco smoking cessation 1.
- Cox regression can also be used to evaluate the shape of dose-response relation with years since quitting as a continuous variable using restricted cubic splines with four knots, providing valuable insights into the relationship between survival time and predictor variables 1.
From the Research
Cox Regression Explanation
- The Cox model is a regression technique for performing survival analyses in epidemiological and clinical research 2.
- This model estimates the hazard ratio (HR) of a given endpoint associated with a specific risk factor, which can be either a continuous variable like age and C-reactive protein level or a categorical variable like gender and diabetes mellitus 2.
- The Cox regression model allows an adjustment for potential confounders; in an exposure-outcome pathway, a confounder is a variable which is associated with the exposure, is not an effect of the exposure, does not lie in the causal pathway between the exposure and the outcome, and represents a risk factor for the outcome 2.
Assumptions of Cox Regression
- A fundamental assumption underlying the application of the Cox model is proportional hazards; in other words, the effects of different variables on survival are constant over time and additive over a particular scale 2.
Applications of Cox Regression
- Cox regression has been used in various studies to analyze the associations between different variables and outcomes, such as the relationship between pulse pressure and cardiovascular events 3, the relationship between cholesterol level and myocardial infarction or mortality risk 4, and the effect of the DASH diet on cardiovascular outcomes 5.
- Cox regression has also been used to evaluate the impact of elevated remnant cholesterol on the risk of ischemic heart disease and myocardial infarction 6.