Survival Analysis

Survival analysis, also known as time-to-event analysis, is a branch of statistics that studies the amount of time it takes before a particular event occurs. Providers of life insurance mainly use survival analysis to predict the death of the insured. Yet it also may predict policy cancellations, non-renewals and how long it takes to file a claim. Providers use results from such analyses to help calculate insurance premiums, as well as the lifetime value of clients.

Survival analysis mainly comes from the medical and biological disciplines, which leverage it to study rates of death, organ failure and the onset of various diseases. Perhaps for this reason, many associate survival analysis with negative events. However, it also can apply to positive events, such as how long it might take someone to win the lottery if they play it each week. Over time, survival analysis has been adapted to the biotechnology sector, and also has uses in economics, marketing, machine maintenance and other fields besides insurance.

Analysts at life-insurance companies use survival analysis to outline the incidence of death at different ages given certain health conditions. From these functions, computing the probability of whether policyholders will outlive their life-insurance coverage is fairly straightforward. Providers can then calculate an appropriate insurance premium by also taking into account the value of the potential customer payouts under the policy.

Survival analysis also plays a large role elsewhere in the insurance industry. For instance, it may help estimate how long it will take drivers from a particular zip code to have an auto accident, based not only on their location, but their age, the type of insurance they carry, and how long it has been since they last filed a claim.

There are other more common statistical methods that might shed some light on how long it might take something to happen. For example, regression analysis might help predict survival times, and it’s a straightforward calculation. However, linear regression often makes use of both positive and negative numbers, whereas, survival analysis deals with time, which is strictly positive.

More importantly, linear regression is not able to account for censoring, meaning survival data that is not complete for various reasons. This is especially true of right-censoring, or the subject that has not yet experienced the expected event during the studied time period.

The main advantage of survival analysis is that it can better tackle the issue of censoring, as its main variable other than time addresses whether the expected event happened or not. For this reason, it is perhaps the technique best-suited to answering time-to-event questions in multiple industries and disciplines.