Survival Analysis
Survival analysis is a research method used to study the effects of a particular stimulus or treatment on the “survival” of individuals over some period of time. It is a powerful tool for uncovering relationships between factors, treatments, and outcomes, and is sometimes referred to as “time-to-event analysis” because it measures how long it takes for an event of interest (such as death, recurrence of cancer, or job loss) to occur after exposure to the stimulus.
Survival analysis is a specialized form of regression analysis which can be applied to data which involves elapsed time from some event or starting event to either a terminal event (like death or failure) or an event of relevance for side effects. Survival analysis has many applications in the medical, social, and business sciences.
In medical research, it is used to measure a patients “survival time” or to measure the effects of a treatment or drug on a patients survival time. In marketing and economics, survival analysis is used to predict customer attrition and determine the effect of new services on customer retention. The method also has applications in social science, where it can be used to measure the effect of particular events on educational attainment or job loss.
Survival analysis involves several different types of data analysis. First, researchers must collect survival data, either on a single subject or on a large population. Data can be collected on individuals or cohorts (groups of people with similar characteristics). Next, the researcher must analyze the data to examine the effects of the stimulus or treatment on the subjects’ survival rate. Methods used for this analysis often include the use of graphical plots and tables.For example, a Kaplan-Meier Plot is a graphical representation of the survival data which can be used to examine the effects of the stimulus or treatment.
Finally, the researcher must interpret the data and draw conclusions.In medical research, interpreted data can inform physicians in their decision making and help them determine which treatments are most effective. In business research, interpreted data can help determine how to increase customer retention or how to determine when a new product or feature is most likely to be successful.
Survival analysis is a powerful tool for examining relationships between stimuli, treatments, and outcomes. It can be used to measure the success of treatments and interventions, predict customer attrition, determine educational attainment, and more. By monitoring elapsed time from exposure to the stimulus to the outcome, researchers can gain a much better understanding of how their stimuli or treatments are impacting their subject populations.
