Name: left and right truncation in survival data
Uploaded: 2025-01-09T15:14:17.000Z
Duration: 1 min 9 s
Channel: JoVE

En el análisis de supervivencia, el tiempo se mide desde el inicio del estudio, lo que garantiza que todos los participantes sean observados desde el momento cero hasta que mueran o sean censurados. Pero esto no siempre es factible.
Por ejemplo, en un estudio que examina la exposición ocupacional a un carcinógeno potencial, se puede entrevistar a los trabajadores de una fábrica sobre su exposición pasada y factores de riesgo como el cáncer y luego se les hace un seguimiento.
Idealmente, el tiempo cero es la fecha de inicio del empleo en lugar de cuando comenzó el estudio.
Sin embargo, los trabajadores que dejaron la fábrica antes de que comenzara el estudio no están incluidos en el estudio. Este problema se conoce como truncamiento a la izquierda.
Por otro lado, el truncamiento a la derecha ocurre cuando se excluyen individuos si el tiempo de su evento excede un valor específico, como el período de estudio.
El truncamiento es diferente de la censura. En el truncamiento, no hay información disponible sobre algunos temas porque están excluidos de los datos. Por otro lado, la censura proporciona información parcial sobre estos temas.

truncamiento en el análisis de supervivencia

Truncation in Survival Analysis

El truncamiento en el análisis de supervivencia se refiere a la exclusión de individuos o eventos del conjunto de datos en función de criterios específicos relacionados con el momento del evento. Esta exclusión puede ocurrir en dos formas principales: truncamiento izquierdo y truncamiento derecho.

El truncamiento izquierdo ocurre cuando los individuos que experimentaron el evento de interés antes de un momento determinado no se incluyen en el estudio. Esto suele deberse a una "entrada tardía" en el estudio donde solo se observan aquellos que sobreviven hasta un punto de entrada determinado. Por ejemplo, en un estudio ocupacional, los trabajadores que se jubilaron o murieron antes del inicio del estudio no formarían parte del conjunto de datos, lo que genera un sesgo que solo incluye a aquellos que aún corren riesgo después del punto de entrada.

El truncamiento derecho es menos común y ocurre cuando solo se incluyen los individuos que experimentaron el evento en un momento determinado. Esto puede suceder en estudios donde el evento ya ocurrió y solo se consideran aquellos con resultados observados. Un ejemplo sería un estudio de mortalidad donde solo se registran las muertes dentro de un período de tiempo específico, excluyendo a aquellos que vivieron más allá del período de observación.

La diferencia entre el truncamiento y la censura radica en la disponibilidad de los datos. La censura se produce cuando hay información incompleta sobre el momento del evento; puede que no se conozca el momento exacto, pero hay alguna información disponible, como el hecho de que un evento no ha ocurrido hasta cierto punto (censura derecha) o que ya había ocurrido antes de que comenzara la observación (censura izquierda). Por el contrario, el truncamiento implica que no se incluye ningún dato en el caso de ciertos sujetos que no cumplen los criterios de inclusión; se los excluye por completo del análisis.

Otros ejemplos de truncamiento son los estudios sobre enfermedades en los que solo se incluyen los individuos diagnosticados después de una fecha específica, excluyendo a los diagnosticados antes (truncamiento izquierdo). De manera similar, en los estudios astronómicos, solo se consideran las estrellas observables dentro de una cierta distancia, mientras que se excluyen las que están demasiado lejos para ser detectadas (truncamiento derecho).

Truncamiento en el análisis de supervivencia

En el análisis de supervivencia, el tiempo se mide desde el inicio del estudio, lo que garantiza que todos los participantes sean observados desde el momento cero hasta que mueran o sean censurados. Pero...

Education

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JoVE Core

Statistics

Estadísticas

Unit 1

Unidad 1

Survival Analysis

Análisis de supervivencia

KEY TERMS AND CONCEPTS

TÉRMINOS Y CONCEPTOS CLAVE

left and right truncation in survival data

Truncation in survival analysis describes data loss caused by the timing of an event or by when a subject can enter a study. It can happen in two forms: left truncation and right truncation. In both cases, some individuals are left out of the dataset before analysis begins.

Left truncation happens when people who had the event before a set time are not included in the study. This often occurs with delayed entry, which means a person is only observed if they survive until the entry point. For example, in an occupational study, workers who retired or died before the study started would not appear in the data. That can bias the sample toward people who are still at risk after entry.

Right truncation is less common. It occurs when only individuals who have already had the event by a certain time are included. A mortality study that records only deaths within a specific time window is one example. People who lived beyond that observation period would be excluded.

Truncation is different from censoring. Censoring means the event time is incomplete, but some information is still available. Right censoring tells us the event has not happened by a certain time, and left censoring means it happened before observation began. Truncation gives no data at all for subjects who do not meet the entry rule, so they are completely excluded from the analysis.

Other examples help show the idea. In disease studies, researchers may include only people diagnosed after a certain date, which is left truncation because earlier cases are excluded. In astronomy, right truncation can appear when only stars close enough to be seen are studied, while stars that are too far away are left out.

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key terms and concepts

Learn how left truncation and right truncation affect survival data, why delayed entry matters, and how truncation differs from censoring.

Left and Right Truncation in Survival Data | JoVE K12

statistics

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Left and Right Truncation in Survival Data

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