16 Feb How Do I Perform Survival Analysis In Stata?

Survival analysis is a statistical technique used to analyze time-to-event data, such as the time until an event occurs. This technique is widely used in medical research, engineering, social sciences, and other fields where time-to-event data is collected. In Stata, survival analysis can be performed using the stset and stcox commands.

The stset command is used to set up the dataset for survival analysis. It specifies the time variable and the event indicator variable, which indicate the time at which an event occurred and whether the event occurred or not. For example, in a medical study, the time variable could be the time from the start of treatment to the occurrence of a disease, and the event indicator variable could be 1 if the disease occurred and 0 if it did not.

The stcox command is used to perform Cox proportional hazards regression analysis, which is a commonly used method in survival analysis. It models the hazard function, which is the probability of an event occurring at a specific time given that the individual has survived up to that time. The Cox model assumes that the hazard function is proportional across groups, meaning that the ratio of hazards between two groups is constant over time.

In addition to Cox proportional hazards regression, Stata also offers other survival analysis techniques such as Kaplan-Meier survival analysis and parametric survival analysis.

Kaplan-Meier survival analysis is a non-parametric method used to estimate the survival function, which is the probability of surviving up to a certain time. It does not assume any specific distribution for the survival times and is particularly useful when the assumption of proportional hazards is not met.

Parametric survival analysis, on the other hand, assumes a specific distribution for the survival times and estimates the parameters of that distribution. It can provide more accurate predictions of survival times but requires the assumption that the survival times follow a specific distribution.

Overall, survival analysis in Stata is a powerful tool for analyzing time-to-event data in a wide range of fields. It allows researchers to model the probability of an event occurring over time and to investigate the effect of various predictors on the survival time. By using the appropriate survival analysis technique and interpreting the results carefully, researchers can gain valuable insights into the underlying processes that govern the occurrence of events over time.