What Cox-regression is
Cox-regression (also known as Cox proportional hazards model) is a regression model used to analyze time-to-event data, such as survival time. It is used to estimate the relative risk of an event occurring, given the values of one or more predictors.
Steps for Cox-regression:
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Collect the data: Gather data on the time until a particular event occurs (e.g. death, relapse, etc.) and the values of one or more predictors (e.g. age, gender, etc.)
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Fit the model: Fit a Cox-regression model to the data, using maximum likelihood estimation.
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Interpret the results: The model will provide estimates of the relative risk of an event occurring, given the values of the predictors.
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Assess the validity of the model: Check for violation of the proportional hazards assumption and adjust the model accordingly.
Examples
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A Cox regression can be used to assess the effect of a particular drug on survival time of patients with a certain disease.
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A Cox regression can be used to examine the impact of various factors, such as smoking status, on the risk of developing a specific type of cancer.
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A Cox regression can be used to investigate the effect of various lifestyle factors on the risk of stroke.