Cox proportional hazards and Kaplan-Meier analyses are both commonly used in survival analysis, but they differ in their approach and purpose.
Kaplan-Meier analysis is a non-parametric method used to estimate the survival function or time-to-event probability for a group of individuals. It is often used when there is censoring (i.e., some individuals do not experience the event of interest by the end of the study) and when comparing survival curves between two or more groups. Kaplan-Meier analysis can be used to estimate median survival time, which is the time at which 50% of individuals have experienced an event.
In contrast, Cox proportional hazards regression is a parametric method that models the relationship between one or more predictor variables and the hazard rate (or risk) of an event occurring over time. The Cox model assumes that the hazard ratio between any two groups remains constant over time, also known as the proportional hazards assumption. The output from a Cox model includes estimates of hazard ratios for each predictor variable, adjusted for other variables included in the model.
While Kaplan-Meier analysis provides descriptive information about survival times and differences between groups, Cox proportional hazards regression allows for testing hypotheses about associations between predictors and outcomes while adjusting for potential confounding factors. Additionally, Cox models can provide estimates of adjusted survival probabilities over time and can assess interactions between predictor variables.
Overall, Kaplan-Meier analysis is useful for visualizing survival data and comparing groups descriptively while Cox proportional hazards regression provides more formal statistical modeling to identify predictors associated with changes in risk over time.
