Survival Analysis for Business Analytics

Survival analysis refers to a class of statistical techniques that measure the effect of predictors on the time until an event, rather than the probability of an event occurring. As the name indicates, this technique has roots in the field of medical research for evaluating the effect of drugs or medical procedures on time until death. However, there are many less morbid applications of this technique, such as the following business analytics examples that I've observed during my 20 years as a data scientist: The most commonly used survival analysis techniques are Kaplan-Meier and Cox Regression. The Kaplan-Meier test is already widely used within the pharmaceutical industry for clinical drug trials, comparing the effects of drugs and their placebos on either time to recovery or to death. In an article for The New Yorker, Malcolm Gladwell includes an interesting description of the critical role of Kaplan-Meier tests in the search for effective cancer treatments. This type of test determines if there is a statistically significant difference between the survival time of two or more groups.