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

W analizie przeżycia czas jest mierzony od początku badania, zapewniając, że wszyscy uczestnicy są obserwowani od czasu zerowego do momentu śmierci lub ocenzurowania. Ale nie zawsze jest to wykonalne.
Na przykład w badaniu oceniającym narażenie zawodowe na potencjalny czynnik rakotwórczy pracownicy fabryk mogą zostać przepytani na temat ich wcześniejszego narażenia i czynników ryzyka, takich jak rak, a następnie podjąć działania następcze.
Idealnie byłoby, gdyby czas zero był datą rozpoczęcia zatrudnienia, a nie datą rozpoczęcia badania.
Jednak pracownicy, którzy opuścili fabrykę przed rozpoczęciem badania, nie są nim objęci. Ten problem jest znany jako obcinanie po lewej.
Z drugiej strony, prawostronne obcięcie występuje, gdy osoby są wykluczane, jeśli ich czas zdarzenia przekracza określoną wartość, taką jak okres badania.
Obcinanie różni się od cenzurowania. W przypadku obcięcia nie są dostępne żadne informacje na niektóre tematy, ponieważ są one wykluczone z danych. Z drugiej strony, cenzura dostarcza częściowych informacji na takie tematy.

obcięcie w analizie przeżycia

Truncation in Survival Analysis

Obcięcie w analizie przeżycia odnosi się do wykluczenia osób lub zdarzeń z zestawu danych na podstawie określonych kryteriów związanych z czasem zdarzenia. To wykluczenie może mieć dwie podstawowe formy: obcięcie lewe i obcięcie prawe. Obcięcie lewe występuje, gdy osoby, które doświadczyły interesującego zdarzenia przed określonym czasem, nie są uwzględniane w badaniu. Często wynika to z „opóźnionego wejścia” do badania, w którym obserwowani są tylko ci, którzy przeżyli do określonego punktu wejścia. Na przykład, w badaniu zawodowym pracownicy, którzy przeszli na emeryturę lub zmarli przed rozpoczęciem badania, nie byliby częścią zestawu danych, co prowadziłoby do błędu, który obejmuje tylko tych, którzy nadal są zagrożeni po punkcie wejścia. Obcięcie prawe jest mniej powszechne i występuje, gdy uwzględniane są tylko osoby, które doświadczyły zdarzenia do określonego czasu. Może się to zdarzyć w badaniach, w których zdarzenie już wystąpiło, a brane są pod uwagę tylko osoby z zaobserwowanymi wynikami. Przykładem może być badanie śmiertelności, w którym rejestrowane są tylko zgony w określonych ramach czasowych, z wyłączeniem osób, które przeżyły okres obserwacji. Różnica między obcięciem a cenzurowaniem leży w dostępności danych. Cenzurowanie występuje, gdy istnieją niekompletne informacje o czasie zdarzenia; dokładny czas może nie być znany, ale są dostępne pewne informacje, takie jak fakt, że zdarzenie nie miało miejsca do pewnego momentu (cenzurowanie prawe) lub miało miejsce już przed rozpoczęciem obserwacji (cenzurowanie lewe). Natomiast obcięcie nie obejmuje żadnych danych dla niektórych podmiotów, które nie spełniają kryteriów wejścia; są one całkowicie wykluczone z analizy. Inne przykłady obcięcia obejmują badania nad chorobami, w których uwzględniane są tylko osoby zdiagnozowane po określonej dacie, z wyłączeniem tych zdiagnozowanych wcześniej (obcięcie lewe). Podobnie w badaniach astronomicznych uwzględniane są tylko gwiazdy obserwowalne w pewnej odległości, a te, które są zbyt daleko, aby je wykryć, są wykluczane (obcięcie prawe).

Obcięcie w analizie przeżycia

W analizie przeżycia czas jest mierzony od początku badania, zapewniając, że wszyscy uczestnicy są obserwowani od czasu zerowego do momentu śmierci lub ocenzurowania. Oprócz...

Education

JoVE Core

Statistics

Statystyka

Unit 1

Jednostka 1

Survival Analysis

Analiza przeżycia

KEY TERMS AND CONCEPTS

KLUCZOWE TERMINY I POJĘCIA

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.

Left and Right Truncation in Survival Data

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truncation in survival analysis

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...

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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

JoVE Core Statistics introduces the concepts and application of the collection, analysis, presentation, and interpretation of data to students. These animated lessons provide an understanding of statistics beneficial to students from a wide range of disciplines. Additionally, the scientist-in-action videos demonstrate the application of related concepts in classical and original research experiments performed in today's laboratories worldwide

Left and Right Truncation in Survival Data

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