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

Hayatta kalma analizinde, zaman çalışmanın başlangıcından itibaren ölçülür ve tüm katılımcıların sıfır zamanından ölene veya sansürlenene kadar gözlemlenmesi sağlanır. Ancak, bu her zaman mümkün değildir.
Örneğin, potansiyel bir kanserojene mesleki maruziyeti inceleyen bir çalışmada, fabrika işçileri geçmiş maruziyetleri ve kanser gibi risk faktörleri hakkında görüşülebilir ve daha sonra takip edilebilir.
İdeal olarak, sıfır zamanı, çalışmanın başladığı zamandan ziyade işe başlama tarihidir.
Ancak, çalışma başlamadan önce fabrikayı terk eden işçiler çalışmaya dahil edilmemiştir. Bu sorun sola kesme olarak bilinir.
Öte yandan, bireylerin olay sürelerinin çalışma süresi gibi belirli bir değeri aşması durumunda dışlanması durumunda hak kesilmesi meydana gelir.
Kesme, sansürlemeden farklıdır. Kesmede, verilerden çıkarıldıkları için bazı konular hakkında herhangi bir bilgi mevcut değildir. Öte yandan, sansür bu tür konularda kısmi bilgi sağlar.

hayatta kalma analizinde kesme

Truncation in Survival Analysis

Hayatta kalma analizinde kesme, belirli bir zaman dilimine ilişkin kriterlere dayanarak bireylerin veya olayların veri kümesinden çıkarılması anlamına gelir. Bu dışlama iki temel biçimde olabilir: sol kesme ve sağ kesme. Sol kesme, belirli bir zamandan önce ilgi duyulan olayı deneyimleyen bireylerin çalışmaya dahil edilmemesi durumunda meydana gelir. Bu genellikle, yalnızca belirli bir giriş noktasına kadar hayatta kalanların gözlemlendiği çalışmaya "gecikmeli giriş" nedeniyle olur. Örneğin, mesleki bir çalışmada, çalışmanın başlamasından önce emekli olan veya ölen çalışanlar veri kümesinin bir parçası olmaz ve bu da yalnızca giriş noktasından sonra hala risk altında olanları içeren bir önyargıya yol açar. Sağ kesme daha az yaygındır ve yalnızca belirli bir zamana kadar olayı deneyimleyen bireylerin dahil edilmesi durumunda meydana gelir. Bu, olayın zaten meydana geldiği ve yalnızca gözlemlenen sonuçlara sahip olanların dikkate alındığı çalışmalarda olabilir. Bir örnek, yalnızca belirli bir zaman dilimi içindeki ölümlerin kaydedildiği ve gözlem süresinin ötesinde yaşayanların hariç tutulduğu bir ölüm oranı çalışması olabilir. Kesme ve sansürleme arasındaki fark, veri kullanılabilirliğinde yatar. Sansürleme, olay zamanı hakkında eksik bilgi olduğunda meydana gelir; kesin zaman bilinmeyebilir, ancak bir olayın belirli bir noktaya kadar gerçekleşmemiş olması (sağ sansürleme) veya gözlem başlamadan önce gerçekleşmiş olması (sol sansürleme) gibi bazı bilgiler mevcuttur. Buna karşılık, kesme, giriş kriterlerini karşılamayan belirli denekler için hiçbir veri içermez; analizden tamamen hariç tutulurlar. Kesmenin diğer örnekleri arasında, yalnızca belirli bir tarihten sonra teşhis edilen bireylerin dahil edildiği ve daha erken teşhis edilenlerin hariç tutulduğu hastalıklarla ilgili çalışmalar yer alır (sol kesme). Benzer şekilde, astronomik çalışmalarda, yalnızca belirli bir mesafedeki gözlemlenebilir yıldızlar dikkate alınırken, tespit edilemeyecek kadar uzakta olanlar hariç tutulur (sağ kesme).

Hayatta Kalma Analizinde Kesme

Hayatta kalma analizinde, zaman çalışmanın başlangıcından itibaren ölçülür ve tüm katılımcıların sıfır zamanından ölene veya sansürlenene kadar gözlemlenmesi sağlanır. Ama...

Education

JoVE Core

Statistics

İstatistik

Unit 1

Ünite 1

Survival Analysis

Sağkalım Analizi

KEY TERMS AND CONCEPTS

TEMEL TERİMLER VE KAVRAMLAR

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 İstatistikleri öğrencilere verilerin toplanması, analiz edilmesi, sunulması ve yorumlanması kavramlarını ve uygulamalarını tanıtır. Bu animasyonlu dersler, çok çeşitli disiplinlerden öğrencilere faydalı olacak istatistik anlayışı sağlar. Ek olarak, eylem halindeki bilim insanı videoları, dünya çapındaki günümüz laboratuvarlarında gerçekleştirilen klasik ve orijinal araştırma deneylerinde ilgili kavramların uygulanmasını göstermektedir.

Left and Right Truncation in Survival Data

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