# Survival Analysis Definition

## What is Survival Analysis?

Survival analysis, also known as time-to-event analysis, is a branch of statistics which studies the 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 the time it takes to file a claim. The results of these analyzes can help providers calculate insurance premiumsas well as customer lifetime value.

### Key points to remember

• Survival analysis is a branch of statistics that studies how long it takes for certain cases to occur.
• It was originally developed in the biomedical sciences to understand the onset of certain diseases, but is now used in engineering, insurance and other disciplines.
• Life insurance company analysts use survival analysis to estimate the probability of death at different ages, taking into account health factors.
• This information is used to estimate the likelihood that a policyholder will outlive their policy, which, in turn, influences insurance premiums.

## Understanding Survival Analysis

The analysis of survival comes mainly from the medical and biological disciplines, which take advantage of it to study death rates, organ failures and the occurrence of various diseases. Perhaps this is why many people associate survival analysis with negative events. However, it can also apply to positive events, such as how long it will take someone to win the lottery if they play it every week.

Over time, survival analysis has adapted to the biotechnology sector and also has uses in economymarketing, machine maintenance and other fields than insurance.

Survival analysis was originally developed in the biomedical sciences to examine the rates of death or organ failure during the onset of certain diseases, but is now used in fields ranging from insurance and funding for marketing and public policy.

### Insurance

The analysts of life insurance companies use survival analysis to describe the incidence of death at different ages given certain health conditions. From these functions, calculating the probability that policyholders outlive their life insurance coverage is fairly straightforward. Providers can then calculate an appropriate insurance premium, the amount charged to each customer for protection, also taking into account the value of potential customer payments under the policy.

Survival analysis also plays an important role elsewhere in the insurance industry. For example, it can help estimate how long it will take drivers in a particular ZIP code to have a car accident, based not only on their location, but also on their age, type of insurance they subscribe and the time elapsed since they filed a last claim.

There are other more common statistical methods that can shed light on how long it takes for something to happen. For instance, regression analysiswhich is commonly used to determine how specific factors such as the price of a product or interest rate influence the price movement of an asset, can help predict survival times and is a simple calculation.

The problem is that linear regression often uses both positive and negative numbers, whereas survival analysis deals with time, which is strictly positive. More importantly, linear regression is unable to account for censoring, that is, incomplete survival data for various reasons. This is especially true for right censoring, or the subject who has not yet experienced the expected event during the time period studied.

The main advantage of survival analysis is that it can better address the issue of censorship because its main variable, other than time, determines whether the expected event has occurred or not. For this reason, it is perhaps the best-suited technique for addressing event delay questions across multiple industries and disciplines.