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

Kaplan-Meier 估计器通过生存时间数据来估计生存函数。它主要用于医学研究中追踪患者治疗后的生存情况。
在分析含有删失数据的研究时，该方法非常有用，其中一些患者的随访时间在感兴趣事件发生前即已结束，通常是由于患者死亡所致。
该估计方法依赖于若干假设。首先，删失患者的生存前景与持续观察的患者相同。
其次，无论受试者何时进入研究，其生存概率均保持一致；最后，事件发生的时间需被准确记录。在实际操作中，监测定期随访检查间隔期间发生的事件可能具有挑战性。
一个例子是，在研究结束时部分患者仍然存活的情况下，比较接受不同癌症治疗的两组之间的生存概率。
该估计方法的主要优势包括能够有效处理不完整数据，以及提供直观的图形表示，有助于比较不同患者群体之间的生存率。
相比之下，其主要局限性在于无法对多个风险因素或混杂因素进行调整，因此在复杂的风险情境中效果较差。

kaplan-meier 方法

Kaplan-Meier Approach

Kaplan-Meier 估计量是一种非参数方法，用于根据事件发生时间数据估计生存函数。在医学研究中，它经常用于测量治疗后存活一定时间的患者比例。该估计量是分析事件发生时间数据的基础，在临床试验、流行病学研究和可靠性工程中不可或缺。通过估计生存概率，研究人员可以评估治疗效果、了解疾病进展并为预后决策提供信息。

 Kaplan-Meier 估计量的一个关键优势是它能够处理删失数据，在这种情况下，并非所有参与者都能观察到事件（例如死亡或失败）的确切时间。例如，一些患者可能会退出研究或在研究结束时没有发生任何事件。该方法假设删失观察是随机发生的，并且其事件时间与未删失参与者的事件时间相当。它还假设观察到的事件的确切时间是已知的，但在实践中可能并不总是如此。

 为了说明其应用，考虑一项比较两种癌症治疗方法的临床试验。使用 Kaplan-Meier 估计量，研究人员可以计算每个治疗组随时间推移的生存概率，即使一些参与者提前退出研究或没有经历事件存活下来。这些概率的图形表示，称为生存曲线，提供了一种直观的方式来可视化各组之间的生存差异。例如，下降速度较慢的生存曲线表示该治疗组的结果更好。

 尽管 Kaplan-Meier 估计量有其优势，但它也有明显的局限性。它没有考虑多个风险因素或混杂变量，因此在分析预测因子和生存之间的复杂关系时效果较差。在风险模式随时间变化或需要根据协变量调整的情况下，它尤其有限。对于这种情况，Cox 比例风险模型或参数生存模型等方法通常与 Kaplan-Meier 方法结合使用。

 综上所述，Kaplan-Meier 估计量是一种功能强大且用途广泛的生存分析工具，可为治疗效果和患者结果提供关键见解。它能够处理不完整数据并生成直观的生存曲线，使其成为医学研究中必不可少的方法。然而，它的局限性意味着它通常需要与其他统计技术配合使用，才能全面了解生存数据。

Kaplan-Meier 方法

Kaplan-Meier 估计法通过寿命数据估算生存函数。它主要用于医学研究中追踪患者在治疗后的生存情况……

Education

教育

JoVE Core

Statistics

统计学

Unit 1

第一单元

Survival Analysis

生存分析

KEY TERMS AND CONCEPTS

关键术语与概念

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 统计学（Statistics）向学生介绍了数据收集、分析、呈现和解释的概念和应用。 这些生动的课程提供了对来自广泛学科的学生有益的统计学的理解。 此外，"行动中的科学家"视频展示了相关概念在当今世界各地实验室进行的经典和原创研究实验中的应用

Survival Curves with Kaplan-Meier Data

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