Name: time-to-event data and censoring
Uploaded: 2025-01-09T15:14:16.000Z
Duration: 1 min 18 s
Channel: JoVE

생물 의학 연구의 다음 세 가지 예를 고려하십시오.
첫째, 암 연구에서는 환자가 관해에 접어든 시점부터 재발까지의 시간을 분석합니다.
둘째, 치아 건강에 관한 소아과 연구는 아이가 태어날 때부터 충치로 인해 첫 치아를 채울 때까지의 시간을 측정합니다.
셋째, 관상동맥 우회술에 관한 연구는 수술부터 환자의 사망까지의 시간을 분석합니다.
이러한 연구는 미리 결정된 시작 지점부터 특정 이벤트 발생까지의 시간을 캡처하는 종단 데이터를 생성합니다. 생존 분석은 이러한 데이터를 분석하기 위한 프레임워크를 제공합니다.
생존 분석에서 '사건'은 질병 재발, 사망 또는 회복과 같은 관심 있는 경험을 나타냅니다.
생존 분석에서 '시간'은 연구 시작, 중재 또는 사례의 첫 번째 보고부터 연구 종료, 관심 사건의 발생 또는 환자의 사망 사이의 기간을 측정합니다.
생존 분석에는 생존 함수, 생명표, 위험 분석 및 특정 통계 모델링을 사용해야 합니다.

생존 분석 소개

Introduction To Survival Analysis

생존 분석은 시간-이벤트 자료를 연구하는 데 사용되는 통계적 방법으로, "이벤트"는 사망, 질병 재발, 시스템 장애 또는 복구와 같은 결과를 나타낼 수 있습니다. 생존 자료의 고유한 특징은 관심 있는 이벤트가 연구 기간 동안 일부 개인에게 관찰되지 않았을 때 발생하는 검열입니다. 여기에는 불완전한 자료를 효과적으로 처리하기 위한 특수 기술이 필요합니다.

생존 분석의 주요 목표는 생존 시간(지정된 이벤트가 발생할 때까지의 시간)을 추정하고 이에 영향을 미치는 요인을 이해하는 것입니다. 주요 개념에는 주어진 시간을 초과하여 생존할 확률을 제공하는 생존 함수(S(t)S(t))와 임의의 시점에서의 순간 이벤트 비율을 설명하는 위험 함수(h(t)h(t))가 있습니다. 이러한 함수는 시간 경과에 따른 생존 패턴과 위험에 대한 통찰력을 제공합니다.

일반적인 방법으로는 생존 곡선을 생성하고 그룹 간 생존율을 비교할 수 있는 비모수적 방법인 카플란-마이어 추정기와 생존 시간에 대한 특정 분포를 가정하지 않고 공변량이 생존에 어떻게 영향을 미치는지 조사하는 반모수적 방법인 Cox 비례 위험 모델이 있습니다.

생존 분석은 의학에서 치료 효과를 평가하고 환자 결과를 예측하는 데 광범위하게 사용되고, 엔지니어링에서 제품 수명을 추정하는 데 사용되고, 사회 과학에서 실업이나 수명 이벤트와 같은 기간을 분석하는 데 사용됩니다. 검열된 자료를 처리하고 시간에 따른 현상을 모델링하는 기능은 다양한 분야에서 결과를 이해하고 예측하는 데 필수적인 도구입니다.

생물 의학 연구의 다음 세 가지 예를 고려하십시오.
첫째, 암 연구에서 환자가 차도에 접어든 시점부터 재발까지의 시간은...

Education

교육

JoVE Core

Statistics

통계학

Unit 1

제1단원

Survival Analysis

생존 분석

KEY TERMS AND CONCEPTS

주요 용어 및 개념

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 special methods to handle them correctly.

Two core measures are the survival function and the hazard function. The survival function, written as S(t), gives the probability of surviving beyond a given time. The hazard function, written as h(t), shows the instantaneous event rate at a specific time, which helps describe how risk changes over time.

Common methods include the Kaplan-Meier estimator and the Cox proportional hazards model. Kaplan-Meier is a non-parametric method that creates survival curves and lets researchers compare survival rates between groups. The Cox proportional hazards model is semi-parametric and tests how covariates affect survival without assuming a specific survival-time distribution.

Survival analysis is used in medicine, engineering, and the social sciences. In medicine, it can help assess treatment effects and predict patient outcomes. In engineering, it can estimate product lifespans. In social science studies, it can examine durations such as unemployment or other time-to-life events.

Time-to-Event Data and Censoring

introduction to survival analysis

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

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...

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

Explore time-to-event data, censoring, survival curves, and hazard rates. Learn how survival analysis is used in medicine, engineering, and social science studies.

Time-to-Event Data and Censoring | 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

Time-to-Event Data and Censoring

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