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

Na análise de sobrevivência, o tempo é medido desde o início do estudo, garantindo que todos os participantes sejam observados desde o momento zero até morrerem ou serem censurados. Mas, isso nem sempre é viável.
Por exemplo, em um estudo que examina a exposição ocupacional a um potencial agente cancerígeno, os trabalhadores da fábrica podem ser entrevistados sobre sua exposição passada e fatores de risco, como câncer, e depois acompanhados.
Idealmente, o tempo zero é a data de início do emprego, e não quando o estudo começou.
Mas, os trabalhadores que deixaram a fábrica antes do início do estudo não estão incluídos no estudo. Esse problema é conhecido como truncamento à esquerda.
Por outro lado, o truncamento à direita ocorre quando os indivíduos são excluídos se o tempo do evento exceder um valor específico, como o período do estudo.
O truncamento é diferente da censura. No truncamento, nenhuma informação está disponível sobre alguns assuntos porque eles são excluídos dos dados. Por outro lado, a censura fornece informações parciais sobre esses assuntos.

truncamento na análise de sobrevivência

Truncation in Survival Analysis

O truncamento na análise de sobrevivência se refere à exclusão de indivíduos ou eventos do conjunto de dados com base em critérios específicos relacionados ao tempo do evento. Essa exclusão pode acontecer de duas formas principais: truncamento à esquerda e truncamento à direita. O truncamento à esquerda ocorre quando indivíduos que vivenciaram o evento de interesse antes de um certo tempo não são incluídos no estudo. Isso geralmente ocorre devido a uma "entrada tardia" no estudo, onde apenas aqueles que sobrevivem até um certo ponto de entrada são observados. Por exemplo, em um estudo ocupacional, trabalhadores que se aposentaram ou morreram antes do início do estudo não fariam parte do conjunto de dados, levando a um viés que inclui apenas aqueles que ainda estão em risco após o ponto de entrada. O truncamento à direita é menos comum e ocorre quando apenas indivíduos que vivenciaram o evento em um certo tempo são incluídos. Isso pode acontecer em estudos onde o evento já ocorreu, e apenas aqueles com resultados observados são considerados. Um exemplo seria um estudo de mortalidade onde apenas mortes dentro de um período de tempo específico são registradas, excluindo aqueles que viveram além do período de observação. A diferença entre truncamento e censura está na disponibilidade de dados. A censura ocorre quando há informações incompletas sobre o tempo do evento; o tempo exato pode não ser conhecido, mas há algumas informações disponíveis, como o fato de que um evento não ocorreu até um certo ponto (censura à direita) ou já ocorreu antes do início da observação (censura à esquerda). Em contraste, o truncamento não envolve dados para certos indivíduos que não atendem aos critérios de entrada; eles são completamente excluídos da análise. Outros exemplos de truncamento incluem estudos sobre doenças em que apenas indivíduos diagnosticados após uma data específica são incluídos, excluindo aqueles diagnosticados antes (truncamento à esquerda). Da mesma forma, em estudos astronômicos, apenas estrelas observáveis ​​dentro de uma certa distância são consideradas, enquanto aquelas muito distantes para serem detectadas são excluídas (truncamento à direita).

Truncamento na Análise de Sobrevivência

Na análise de sobrevivência, o tempo é medido desde o início do estudo, garantindo que todos os participantes sejam observados desde o momento zero até morrerem ou serem censurados. Mas...

Education

JoVE Core

Statistics

Estatísticas

Unit 1

Unidade 1

Survival Analysis

Análise de Sobrevivência

KEY TERMS AND CONCEPTS

TERMOS E CONCEITOS-CHAVE

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

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Left and Right Truncation in Survival Data

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