# Censoring (statistics)

**Censoring (statistics)** is a concept in statistics and epidemiology that refers to the situation in which the value of a measurement or observation is only partially known. This can occur for a variety of reasons, such as when a value falls below or above a certain detection limit or when a study ends before all subjects have experienced the event of interest.

## Types of Censoring[edit | edit source]

There are several types of censoring in statistics, including:

**Right censoring**: This occurs when a subject's survival time is known to exceed a certain time, but it is unknown by how much. This is the most common type of censoring in survival analysis.

**Left censoring**: This occurs when a subject's survival time is known to be less than a certain time, but it is unknown by how much.

**Interval censoring**: This occurs when a subject's survival time is known to fall within a certain interval, but the exact time is unknown.

**Random censoring**: This occurs when the censoring time is a random variable that is potentially independent of the survival time.

## Applications in Medical Research[edit | edit source]

Censoring is a common issue in medical research, particularly in survival analysis where the outcome variable of interest is 'time until an event occurs'. For example, in a study of survival times of cancer patients, some patients may still be alive at the end of the study. These patients' survival times are right-censored.

## Handling Censoring in Statistical Analysis[edit | edit source]

Censoring can introduce bias into the analysis if not properly accounted for. Several statistical methods have been developed to handle censoring, including the Kaplan-Meier estimator and the Cox proportional hazards model. These methods provide ways to estimate survival functions and compare survival rates between groups while taking censoring into account.

## See Also[edit | edit source]

- Survival analysis
- Kaplan-Meier estimator
- Cox proportional hazards model
- Likelihood function
- Maximum likelihood estimation

## References[edit | edit source]

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Contributors: Prab R. Tumpati, MD