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

При анализе выживаемости время измеряется с начала исследования, что позволяет убедиться, что за всеми участниками наблюдают с нулевого момента до их смерти или цензуры. Но, это не всегда осуществимо.
Например, в исследовании, изучающем профессиональное воздействие потенциального канцерогена, заводских рабочих могут опрашивать об их прошлом воздействии и факторах риска, таких как рак, а затем наблюдать за ними.
В идеале, нулевое время — это дата начала работы, а не дата начала исследования.
Но рабочие, которые покинули завод до начала исследования, не включаются в исследование. Эта проблема известна как левое усечение.
С другой стороны, усечение по правому краю происходит, когда индивидуумы исключаются, если время их события превышает определенное значение, например, период исследования.
Усечение отличается от цензуры. При усечении информация по некоторым темам недоступна, потому что они исключены из данных. С другой стороны, цензура дает частичную информацию по таким темам.

Усечение в анализе выживаемости

Truncation in Survival Analysis

Усечение в анализе выживаемости означает исключение лиц или событий из набора данных на основе определенных критериев, связанных со временем события. Это исключение может происходить в двух основных формах: левое усечение и правое усечение. Левое усечение происходит, когда лица, которые пережили интересующее событие до определенного времени, не включаются в исследование. Это часто происходит из-за «отложенного входа» в исследование, когда наблюдаются только те, кто дожил до определенной точки входа. Например, в профессиональном исследовании работники, которые вышли на пенсию или умерли до начала исследования, не будут частью набора данных, что приведет к смещению, которое включает только тех, кто все еще находится в группе риска после точки входа. Правое усечение встречается реже и происходит, когда включаются только лица, которые пережили событие к определенному времени. Это может произойти в исследованиях, где событие уже произошло, и рассматриваются только те, кто имел наблюдаемые исходы. Примером может служить исследование смертности, в котором регистрируются только смерти в течение определенного периода времени, исключая тех, кто жил после периода наблюдения. Разница между усечением и цензурированием заключается в доступности данных. Цензурирование происходит, когда есть неполная информация о времени события: точное время может быть неизвестно, но есть некоторая информация, например, тот факт, что событие не произошло до определенного момента (правое цензурирование) или уже произошло до начала наблюдения (левое цензурирование). Напротив, усечение предполагает полное отсутствие данных по некоторым субъектам, которые не соответствуют критериям входа; они полностью исключаются из анализа. Другие примеры усечения включают исследования заболеваний, в которые включаются только лица, диагностированные после определенной даты, исключая тех, которые диагностированы ранее (левое усечение). Аналогично, в астрономических исследованиях рассматриваются только наблюдаемые звезды в пределах определенного расстояния, в то время как те, которые слишком далеки для обнаружения, исключаются (правое усечение).

При анализе выживаемости время измеряется с начала исследования, что позволяет убедиться, что за всеми участниками наблюдают с нулевого момента до их смерти или цензуры. Но...

Education

JoVE Core

Statistics

Статистика

Unit 1

Блок 1

Survival Analysis

Анализ выживаемости

KEY TERMS AND CONCEPTS

ОСНОВНЫЕ ТЕРМИНЫ И ПОНЯТИЯ

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

left-and-right-truncation-in-survival-data

introduction to survival analysis

Time-to-Event Data and Censoring

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life tables

Life Tables and Survival Patterns

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survival curves

Kaplan-Meier Survival Curve Basics

Kaplan-Meier survival curves show how the chance of staying event-free changes over time. They are used to track survival in a population and to compare groups in medicine, public health, and reliability engineering. The Kaplan-Meier estimator is the most common way to build these curves. It is a non-parametric method, which means it does not assume a fixed pattern for the data. The result is a stepwise line that drops each time an event happens, such as death, disease recurrence, or...

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Life Table Method for Survival Rates

The life table method, also called the actuarial approach, is used to estimate survival rates in clinical and population studies. It began in life insurance, where risk over time must be measured carefully. In health research, it gives a clearer picture of survival when some participants are lost to follow-up or die from causes unrelated to the study. This method works by dividing the study period into set intervals, usually one year. That makes it useful for tracking outcomes over time,...

kaplan-meier approach

Survival Curves with Kaplan-Meier Data

The Kaplan-Meier estimator is a non-parametric method for estimating a survival function from time-to-event data. It is commonly used in medical research to track how many patients survive for a set time after treatment. It is also important in clinical trials, epidemiological studies, and reliability engineering. This method helps researchers study survival probabilities over time, even when some data are incomplete. That incomplete data is called censored data, which means the exact event...

assumptions of survival analysis

Survival Analysis Assumptions in Studies

Survival analysis studies the time until an event happens. The event may be death in a living organism or failure in a mechanical system. These models are used in medicine, biology, engineering, and public health to study time-to-event data. Careful study design matters because the method depends on several key assumptions. One common feature of survival times is positive skewness. This means events often happen early, while fewer cases occur later. Survival data also often include censoring,...

comparing the survival analysis of two or more groups

comparing-the-survival-analysis-of-two-or-more-groups

Kaplan-Meier Curves and Hazard Ratios

Kaplan-Meier curves and hazard ratios help researchers compare survival time between groups. Survival analysis looks at the time until an event happens, such as death, disease recurrence, or recovery. It is a key tool in medical research. A major strength of survival analysis is that it can handle censored data. Censored data means the event has not happened for some participants by the end of the study, or it has not been observed. This makes survival analysis different from standard...

the mantel-cox log-rank test

Log-Rank Test for Comparing Survival Curves

The Mantel-Cox log-rank test compares survival curves from two groups. It checks whether the groups have a statistically significant difference in survival time. This makes it useful when the timing of an event matters, such as death or disease recurrence. The log-rank test is non-parametric. That means it does not assume a specific distribution for the survival data. It is also useful in medical research, where it often helps compare a new treatment with a control or standard treatment in...

applications of life tables

Life Tables in Public Policy and Science

Life tables are used in many fields to study mortality and survival rates. They give a numerical way to examine how long people or living things are likely to survive. This makes them useful for public health, business planning, and scientific research. In demography and public health, life tables help experts study population changes and predict future trends. They can show patterns such as an aging population or the effect of a health policy. In Nigeria, health officials used survival rates...

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Cancer Survival Curves and Risk Factors

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hazard rate

Modeling Event Risk Over Time

Hazard rate is a statistical measure that describes how event risk changes over time. It is also called the hazard function or failure rate. In survival analysis, it shows the instantaneous rate at which an event occurs, but only after the subject has not had the event yet. From a probability view, hazard rate gives the chance that a subject will have the event in a very small time interval. That chance is conditional on surviving to the start of that interval. From a frequency view, it can...

hazard ratio

Hazard Ratio in Clinical Trials

The hazard ratio is a way to compare event risk in clinical trials. It is often used to study events such as death or disease recurrence in two groups over time. The two groups are usually a treatment group and a control group. A hazard ratio compares hazard rates, which are the instantaneous risk of an event happening at a given moment. It shows how the risk in one group relates to the risk in the other group. In a cancer drug study, for example, the hazard ratio can show how a new treatment...

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...

censoring survival data

Types of Censoring in Survival Analysis

Survival analysis studies time-to-event data, which tracks how long it takes for an outcome to happen. It is used in medicine, engineering, and social science. A common problem in this type of data is censoring, which means the full event time is not observed for every person or item. Censoring can happen for several reasons and in different patterns. The most common kind is right censoring. This happens when the event has not occurred by the end of the study or when a participant is lost to...

survival tree

Survival Tree in Time-to-Event Analysis

A survival tree is a non-parametric tool in survival analysis that links predictor variables, or covariates, to survival time. Survival time is the time until an event of interest occurs. It is useful when some data are censored, which means the event has not happened for some people by the end of the study or the exact event time is unknown. Building a survival tree starts with a dataset that includes covariates, survival time, and a censoring indicator for each subject. The data must be...

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Modeling Survival Time with Weibull and Exponential

Parametric survival analysis models survival time by assuming a specific probability distribution for when an event occurs. The Weibull and exponential distributions are two common choices because they are flexible and relatively straightforward to use. The Weibull distribution is a flexible model in parametric survival analysis. It can describe both increasing and decreasing hazard rates, depending on its shape parameter beta. When beta is greater than 1, the hazard rate rises over time. That...

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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