Name: survival curves with kaplan-meier data
Uploaded: 2025-01-09T15:14:16.000Z
Duration: 1 min 24 s
Channel: JoVE

Kaplan-Meier 추정기는 수명 데이터에서 생존 함수를 추정합니다. 주로 치료 후 환자의 생존을 추적하기 위해 의료 연구에서 사용됩니다.
이는 일반적으로 사망으로 인해 관심 사건이 발생하기 전에 일부 환자의 추적 관찰 시간이 끝나는 중도절단된 데이터로 연구를 분석하는 데 도움이 됩니다.
이 추정기는 몇 가지 가정에 의존합니다. 첫째, 검열된 환자는 지속적으로 관찰된 환자와 동일한 생존 전망을 공유합니다.
둘째, 생존 확률은 피험자가 연구에 참여하는 시기에 관계없이 일관되며, 마지막으로 사건의 타이밍이 정확하게 기록됩니다. 실제로 정기 검진 사이에 발생하는 이벤트를 모니터링하는 것은 어려울 수 있습니다.
한 가지 예는 연구가 끝날 때까지 일부 환자의 생존율에 관계없이 서로 다른 암 치료를 받는 두 그룹 간의 생존 확률을 비교하는 것입니다.
이 추정기의 주요 장점으로는 불완전한 데이터를 효과적으로 처리하고 다양한 환자 그룹 간의 생존율을 비교하는 데 도움이 되는 직관적인 그래픽 표현이 있습니다.
대조적으로, 주요 한계는 여러 위험 요인 또는 교란 요인을 조정할 수 없다는 것이므로 복잡한 위험 시나리오에서 덜 효과적입니다.

카플란-마이어 접근법

Kaplan-Meier Approach

카플란-마이어 추정기는 시간-사건 자료에서 생존 함수를 추정하는 데 사용되는 비모수적 방법입니다. 의학 연구에서는 치료 후 일정 기간 동안 생존하는 환자의 비율을 측정하는 데 자주 사용됩니다. 이 추정기는 시간-사건 자료를 분석하는 데 기본이 되므로 임상 시험, 역학 연구 및 신뢰성 엔지니어링에 없어서는 안 될 것입니다. 연구자는 생존 확률을 추정하여 치료 효과를 평가하고, 질병 진행을 이해하고, 예후 결정을 알릴 수 있습니다.

카플란-마이어 추정기의 주요 장점은 모든 참가자에게 사건의 정확한 시간(예: 사망 또는 실패)이 관찰되지 않는 검열된 자료를 처리할 수 있는 기능입니다. 예를 들어, 일부 환자는 연구에서 철회하거나 연구가 끝날 때까지 사건이 없는 상태를 유지할 수 있습니다. 이 방법은 검열된 관찰이 무작위로 발생하고 기본 사건 시간이 검열되지 않은 참가자의 사건 시간과 비슷하다고 가정합니다. 또한 관찰된 사건의 정확한 타이밍이 알려져 있다고 가정하는데, 이는 실제로 항상 사실이 아닐 수 있습니다.

적용 사례를 설명하기 위해 두 가지 암 치료법을 비교하는 임상 시험을 고려해 보겠습니다. 카플란-마이어 추정기를 사용하면 연구자는 일부 참가자가 연구를 일찍 그만두거나 사건을 경험하지 않고 생존하더라도 시간이 지남에 따라 각 치료 그룹의 생존 확률을 계산할 수 있습니다. 생존 곡선이라고 하는 이러한 확률의 그래픽 표현은 그룹 간 생존 차이를 시각화하는 직관적인 방법을 제공합니다. 예를 들어, 더 느리게 감소하는 생존 곡선은 해당 치료 그룹의 더 나은 결과를 나타냅니다.

카플란-마이어 추정기는 강점에도 불구하고 주목할 만한 한계가 있습니다. 여러 위험 요인이나 교란 변수를 고려하지 않아 예측 요인과 생존 간의 복잡한 관계를 분석하는 데 효과적이지 않습니다. 위험 패턴이 시간이 지남에 따라 변경되거나 공변량에 대한 조정이 필요한 경우에는 특히 제한적입니다. 이러한 시나리오의 경우 콕스 비례 위험 모델이나 모수 생존 모델과 같은 방법이 카플란-마이어 접근 방식과 함께 자주 사용됩니다.

요약하자면, 카플란-마이어 추정기는 생존 분석을 위한 강력하고 다재다능한 도구로, 치료 효과와 환자 결과에 대한 중요한 통찰력을 제공합니다. 불완전한 자료를 관리하고 직관적인 생존 곡선을 생성하는 능력은 의학 연구에 필수적인 방법입니다. 그러나 한계로 인해 생존 자료에 대한 포괄적인 이해를 얻기 위해 다른 통계 기법으로 보완되는 경우가 많습니다.

Kaplan-Meier 추정기는 수명 데이터에서 생존 함수를 추정합니다. 그것은 주로 의학 연구에서 환자의 생존을 추적하는 데 사용됩니다 ...

Education

교육

JoVE Core

Statistics

통계학

Unit 1

제1단원

Survival Analysis

생존 분석

KEY TERMS AND CONCEPTS

주요 용어 및 개념

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 time is not observed for every participant. Some patients may leave a study early, or they may still be event-free when the study ends. The method assumes that censored cases happen at random and that their event times are similar to those of uncensored participants. It also assumes that the exact timing of observed events is known, although that is not always true in practice.

A common use of the Kaplan-Meier estimator is to compare two treatment groups in a clinical trial, such as two cancer treatments. Researchers can calculate survival probabilities for each group across time, even when some participants withdraw or do not experience the event. The results are shown in a survival curve, which makes the differences between groups easy to see. A curve that drops more slowly suggests better outcomes for that treatment.

The Kaplan-Meier estimator has limits as well. It does not include multiple risk factors or confounding variables, so it is less useful for complex survival patterns. It is also limited when risk changes over time or when researchers need to adjust for covariates. In those cases, methods such as the Cox proportional hazards model or parametric survival models are often used alongside the Kaplan-Meier approach. Even with these limits, the Kaplan-Meier estimator remains a key tool for understanding survival data and patient outcomes.

Survival Curves with Kaplan-Meier 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

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

Learn how Kaplan-Meier data estimates survival probabilities, handles censored data, and shows treatment differences with clear survival curves in medical research.

Survival Curves with Kaplan-Meier 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

Survival Curves with Kaplan-Meier Data

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