Censoring (statistics): Difference between revisions

Latest revision as of 16:29, 16 February 2025

Censoring (statistics)[edit]

Censoring in statistics occurs when the value of a measurement or observation is only partially known. Censoring is a form of missing data problem that arises in various fields, including clinical trials, survival analysis, and econometrics.

Censoring can occur in different forms, such as right censoring, left censoring, and interval censoring. Understanding and handling censored data is crucial for accurate statistical analysis and inference.

Types of Censoring[edit]

Right Censoring[edit]

Right censoring occurs when the observation is only known to be above a certain value. This is common in survival analysis, where the event of interest (such as death or failure) has not occurred by the end of the study period. For example, if a study ends before a patient dies, the survival time is right-censored.

Left Censoring[edit]

Left censoring happens when the observation is only known to be below a certain value. This can occur in studies where measurements below a detection limit are recorded as being less than that limit.

Interval Censoring[edit]

Interval censoring occurs when the observation is only known to lie within a certain interval. This can happen in longitudinal studies where the exact time of an event is not known, but it is known to have occurred between two observation times.

Handling Censored Data[edit]

Statistical methods have been developed to handle censored data, ensuring that analyses remain valid and unbiased. Some common methods include:

Kaplan-Meier estimator: A non-parametric statistic used to estimate the survival function from lifetime data.
Cox proportional hazards model: A regression model commonly used in the analysis of survival data.
Tobit model: A statistical model designed to estimate linear relationships between variables when there is censoring.

Applications[edit]

Censoring is prevalent in many fields:

In clinical trials, censoring is often encountered when patients drop out of the study or the study ends before the event occurs.
In econometrics, censoring can occur in income data where incomes below a certain threshold are not reported.
In environmental science, measurements below detection limits are often left-censored.

Related pages[edit]

@@ Line 1: / Line 1: @@
-'''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.
+== Censoring (statistics) ==
-==Types of Censoring==
+[[File:Censored_Data_Example.svg|thumb|right|Example of censored data]]
-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.
+Censoring in [[statistics]] occurs when the value of a measurement or observation is only partially known. Censoring is a form of missing data problem that arises in various fields, including [[clinical trials]], [[survival analysis]], and [[econometrics]].
-* '''[[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.
+Censoring can occur in different forms, such as right censoring, left censoring, and interval censoring. Understanding and handling censored data is crucial for accurate statistical analysis and inference.
-* '''[[Interval censoring]]''': This occurs when a subject's survival time is known to fall within a certain interval, but the exact time is unknown.
+=== Types of Censoring ===
-* '''[[Random censoring]]''': This occurs when the censoring time is a random variable that is potentially independent of the survival time.
+==== Right Censoring ====
+Right censoring occurs when the observation is only known to be above a certain value. This is common in [[survival analysis]], where the event of interest (such as death or failure) has not occurred by the end of the study period. For example, if a study ends before a patient dies, the survival time is right-censored.
-==Applications in Medical Research==
+==== Left Censoring ====
-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.
+Left censoring happens when the observation is only known to be below a certain value. This can occur in studies where measurements below a detection limit are recorded as being less than that limit.
-==Handling Censoring in Statistical Analysis==
+==== Interval Censoring ====
-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.
+Interval censoring occurs when the observation is only known to lie within a certain interval. This can happen in longitudinal studies where the exact time of an event is not known, but it is known to have occurred between two observation times.
-==See Also==
+=== Handling Censored Data ===
+Statistical methods have been developed to handle censored data, ensuring that analyses remain valid and unbiased. Some common methods include:
+* [[Kaplan-Meier estimator]]: A non-parametric statistic used to estimate the survival function from lifetime data.
+* [[Cox proportional hazards model]]: A regression model commonly used in the analysis of survival data.
+* [[Tobit model]]: A statistical model designed to estimate linear relationships between variables when there is censoring.
+=== Applications ===
+Censoring is prevalent in many fields:
+* In [[clinical trials]], censoring is often encountered when patients drop out of the study or the study ends before the event occurs.
+* In [[econometrics]], censoring can occur in income data where incomes below a certain threshold are not reported.
+* In [[environmental science]], measurements below detection limits are often left-censored.
+== Related pages ==
 * [[Survival analysis]]
+* [[Missing data]]
 * [[Kaplan-Meier estimator]]
 * [[Cox proportional hazards model]]
-* [[Likelihood function]]
-* [[Maximum likelihood estimation]]
-==References==
+{{Statistics}}
-<references />
-[[Category:Statistical terminology]]
+[[Category:Statistics]]
-[[Category:Epidemiology]]
-[[Category:Medical research]]
 [[Category:Survival analysis]]
-{{statistics-stub}}
-{{medicine-stub}}