Survival Analysis

What Is 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 of interest occurs.

Insurance companies use survival analysis to predict the death of the insured and estimate other important factors such as policy cancellations, non-renewals, and how long it takes to file a claim. Results from such analyses can help providers calculate insurance premiums, as well as the lifetime value of clients. 

Understanding Survival Analysis

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 people 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.

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, the amount each client is charged for protection, by also taking into account the value of the potential customer payouts under the policy.

Survival analysis plays a large role elsewhere in the insurance industry, too. 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.

Advantages and Disadvantages of Survival Analysis

There are other more common statistical methods that may shed some light on how long it could take something to happen. For example, regression analysis, which is commonly used to determine how specific factors such as the price of a commodity or interest rates influence the price movement of an asset, might help predict survival times and is a straightforward calculation.

The problem is that 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 benefit 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.