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

생존 분석에서는 연구 시작부터 시간을 측정하여 모든 참가자가 시간 0부터 죽거나 중도절단될 때까지 관찰되도록 합니다. 그러나 이것이 항상 가능한 것은 아닙니다.
예를 들어, 잠재적인 발암 물질에 대한 직업적 노출을 조사하는 연구에서 공장 근로자는 과거 노출 및 암과 같은 위험 요인에 대해 인터뷰한 다음 후속 조치를 취할 수 있습니다.
이상적으로는 시간 0이 연구가 시작된 시점이 아니라 고용 시작 날짜입니다.
그러나 연구가 시작되기 전에 공장을 떠난 근로자는 연구에 포함되지 않습니다. 이 문제를 왼쪽 잘림이라고 합니다.
반면에, 오른쪽 잘림은 개인이 사건 시간이 연구 기간과 같은 특정 값을 초과하는 경우 제외될 때 발생합니다.
잘림은 중도절단과 다릅니다. 잘림에서는 일부 주제가 데이터에서 제외되기 때문에 해당 주제에 대한 정보를 사용할 수 없습니다. 반면에, 검열은 그러한 주제에 대한 부분적인 정보를 제공합니다.

생존 분석에서의 절단

Truncation in Survival Analysis

생존 분석에서의 절단은 사건의 시간과 관련된 특정 기준에 따라 자료 집합에서 개인이나 사건을 제외하는 것을 말합니다. 이 제외는 왼쪽 절단과 오른쪽 절단의 두 가지 주요 형태로 발생할 수 있습니다.

왼쪽 절단은 특정 시간 전에 관심 사건을 경험한 개인이 연구에 포함되지 않을 때 발생합니다. 이는 종종 특정 진입 지점까지 생존한 사람만 관찰되는 연구에 대한 "지연된 진입"으로 인해 발생합니다. 예를 들어, 직업 연구에서 연구가 시작되기 전에 은퇴하거나 사망한 근로자는 자료 집합에 포함되지 않아 진입 지점 이후에도 여전히 위험에 처한 사람만 포함하는 편향이 발생합니다.

오른쪽 절단은 덜 일반적이며 특정 시간까지 사건을 경험한 개인만 포함될 때 발생합니다. 이는 사건이 이미 발생했고 관찰된 결과가 있는 사람만 고려되는 연구에서 발생할 수 있습니다. 예를 들어, 특정 기간 내의 사망자만 기록하고 관찰 기간을 넘어 생존한 사람을 제외하는 사망률 연구가 있습니다.

절단과 검열의 차이는 자료 가용성에 있습니다. 검열은 사건 시간에 대한 정보가 불완전할 때 발생합니다. 정확한 시간은 알 수 없지만 사건이 특정 시점까지 발생하지 않았다는 사실(오른쪽 검열) 또는 관찰이 시작되기 전에 이미 발생했다는 사실(왼쪽 검열)과 같이 일부 정보를 사용할 수 있습니다. 반면, 절단은 진입 기준을 충족하지 않는 특정 피험자에 대한 자료가 전혀 포함되지 않습니다. 이들은 분석에서 완전히 제외됩니다.

절단의 다른 예로는 특정 날짜 이후에 진단받은 개인만 포함하고 이전에 진단받은 개인은 제외하는(왼쪽 절단) 질병 연구가 있습니다. 마찬가지로 천문 연구에서는 특정 거리 내에 있는 관측 가능한 별만 고려하고 감지하기에는 너무 멀리 떨어진 별은 제외합니다(오른쪽 절단).

생존 분석에서는 연구 시작부터 시간을 측정하여 모든 참가자가 시간 0부터 죽거나 중도절단될 때까지 관찰되도록 합니다. 하지만...

Education

교육

JoVE Core

Statistics

통계학

Unit 1

제1단원

Survival Analysis

생존 분석

KEY TERMS AND CONCEPTS

주요 용어 및 개념

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

Survival analysis is a statistical method for time-to-event data. The event can be death, disease relapse, system failure, or recovery. It helps estimate survival time, which is the time until a specific event occurs, and it also shows what factors may affect that time. A key idea in survival analysis is censoring. Censoring happens when the event of interest has not been observed for some people during the study period. Because the data are incomplete in those cases, survival analysis uses...

life tables

Life Tables and Survival Patterns

A life table is a statistical tool that shows mortality and survival patterns in a population. It organizes data across age intervals so students can see how likely a group is to survive or die at each stage of life. The table gives a clear snapshot of population dynamics over time. Life tables are used in demography, public health, actuarial science, and ecology. They help scientists study life expectancy, plan health interventions, estimate insurance risk, and compare how species survive in...

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...

actuarial approach

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...

cancer survival analysis

Cancer Survival Curves and Risk Factors

Cancer survival analysis measures how long it takes from diagnosis or the start of treatment to a specific outcome, such as remission or death. It helps researchers judge treatment effectiveness and understand what affects patient outcomes. These results also support clinical decisions and prognostic evaluations. Survival data are often skewed and not normally distributed. They also include censored cases, where the full survival time is unknown because the study ends, a patient is lost to...

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...

parametric survival analysis: weibull and exponential methods

parametric-survival-analysis-weibull-and-exponential-methods

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

Description

Transcript