The `CoxPHFitter.fit` function in the `lifelines` library in Python is used to fit a Cox proportional hazards regression model to a dataset. Cox proportional hazards regression is a statistical model commonly used in survival analysis to explore the relationship between the time until an event occurs (survival time) and one or more predictor variables.
The `CoxPHFitter.fit` function takes as input a dataset and estimates the coefficients of the predictor variables using maximum likelihood estimation. It uses the observed survival times and event indicators (e.g., whether an event occurred or the individual is still under observation) to estimate the hazard function.
The function outputs various statistics and information about the fitted model, such as the estimated coefficients (hazard ratios), p-values, confidence intervals, and the baseline hazard function. These results can be used to make predictions or assess the impact of predictor variables on the survival time.
Overall, the `CoxPHFitter.fit` function in the `lifelines` library allows users to fit a Cox proportional hazards regression model to their data and obtain valuable insights into the relationship between predictor variables and the survival time.
Python CoxPHFitter.fit - 41 examples found. These are the top rated real world Python examples of lifelines.CoxPHFitter.fit extracted from open source projects. You can rate examples to help us improve the quality of examples.