PROPORTIONAL HAZARDS MODEL Meaning and
Definition
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A proportional hazards model refers to a statistical modeling technique commonly used in survival analysis to understand the relationship between a set of predictor variables and the time to occurrence of an event, such as death, failure, or relapse. This modeling approach assumes that the hazard rates, which represent the instantaneous probabilities of the event occurring at a given time, are proportional over time for different levels of the predictor variables.
The proportional hazards model is particularly useful when studying events that have not yet occurred for all individuals in the dataset, such as when analyzing the time it takes for a patient to experience a specific medical outcome. By estimating the hazard ratio, which is the ratio of the hazards between two groups with different levels of a predictor variable, this model allows researchers to determine the effect of each predictor on the event occurrence while controlling for other factors.
The proportional hazards model is based on the Cox regression model, which is a semiparametric technique that does not require assumptions about the distribution of the event times. Instead, the model estimates the hazard function by considering the relative risk associated with each predictor and their contribution to the baseline hazard.
Overall, the use of proportional hazards model allows for the identification and quantification of the effects of various factors on the timing of events, aiding in the interpretation and prediction of event occurrence.
Common Misspellings for PROPORTIONAL HAZARDS MODEL
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