Proportional hazards model
Proportional hazards model
The Proportional hazards model (pronunciation: pruh-por-shuh-nl hah-zuh-rds moh-del), also known as the Cox regression model, is a statistical technique used in the field of epidemiology and biostatistics for the analysis of survival data.
Etymology
The term "proportional hazards model" is derived from its fundamental assumption that the hazard ratio is constant over time. The model is named after the British statistician Sir David David Cox, who first proposed the model in 1972.
Definition
The Proportional hazards model is a type of survival analysis model that provides a way to estimate the effect of several variables on survival or other time-to-event outcomes, while controlling for the effects of other variables. The model assumes that the effects of the predictor variables are multiplicative with respect to the hazard rate and are constant over time, hence the term "proportional hazards."
Related Terms
- Hazard ratio: The hazard ratio in a proportional hazards model is the effect of an explanatory variable on the hazard or risk of an event.
- Survival function: In survival analysis, the survival function (also known as the survivor function) gives the probability that a subject survives from the time origin to a specified future time.
- Censoring: Censoring in statistics occurs when the value of an observation is only partially known.
Applications
The Proportional hazards model is widely used in medical research for investigating the association between the survival time of patients and one or more predictor variables. For example, it can be used to study the effect of treatment methods on the survival time of cancer patients.
See Also
External links
- Medical encyclopedia article on Proportional hazards model
- Wikipedia's article - Proportional hazards model
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