Name: survival analysis assumptions in studies
Uploaded: 2025-01-09T15:14:16.000Z
Duration: 1 min 15 s
Channel: JoVE

生存分析是一种统计学方法，用于评估事件发生前的时间。在医学领域，该方法常用于分析预期寿命。
选择一个定义明确、清晰且可观察的临床相关事件，对于准确分析至关重要。
一个关键方面是删失（censoring），当数据由于死亡或参与者退出研究等事件而不完整时，就会发生删失。例如，离开研究的患者其数据将被右删失（right-censored）。
独立删失是指删失的原因（如退出研究）与所关注的结果无关。
接下来，Cox 比例风险假设认为，各组之间的相对风险或风险比保持恒定。
平稳性假设确保了除非明确地以其他方式建模，否则所有研究组中事件随时间变化的概率均相同。
此外，必须仔细确定随访时长和样本量，以确保有足够的事件发生，从而进行可靠的分析。

生存分析的假设

Assumptions of Survival Analysis

生存模型分析一个或多个事件发生的时间，例如生物体死亡或机械系统故障。这些模型广泛应用于医学、生物学、工程学和公共卫生等领域，用于研究事件发生时间现象。为确保结果准确，生存分析依赖于关键假设和精心的研究设计。

<ol>
	<li>生存时间呈正偏态

	生存时间通常呈现正偏态，不同于许多其他分析中假设的正态分布。这意味着事件往往在早期更频繁地发生，随着时间的推移，发生的次数会减少。
	</li>
	<li>数据删失
	当未观察个体的完整生存时间时，就会发生删失，但这与缺失数据不同。删失的常见原因包括参与者退出研究、在事件发生前研究期结束或参与者经历不相关事件（例如，因不相关原因死亡）。例如，在一项关于心脏病的研究中，在事故中死亡的参与者的数据将在死亡时被删失。
	</li>
	<li>独立删失
	该假设认为删失的原因与感兴趣事件的可能性无关。例如，如果症状严重的参与者更有可能退出研究，则生存估计可能会出现偏差。确保删失不依赖参与者的健康状况对于可靠的分析至关重要。
	</li>
	<li>比例风险（特定于 Cox 模型）
	Cox比例风险模型假设任何两个个体之间的风险比随时间保持不变。例如，如果在研究开始时一个组发生事件的风险是另一个组的两倍，则该风险比必须在整个研究期间保持不变。
	</li>
	<li>平稳性
	平稳性假设事件随时间变化的概率在所有组中都类似，除非明确建模。例如，在比较接受新药治疗的患者和接受标准治疗的患者之间的生存时间时，影响生存的外部因素应该对两组产生同等影响，除非已对此进行考虑。
	</li>
	<li>明确且具有临床重要性的事件
	感兴趣的事件应具有临床意义并定义明确，以便进行准确的测量和分析。模糊或错误分类的事件（例如，不明确的复发标准）可能会损害生存时间数据的有效性。
	</li>
	<li>足够的随访期
	随访时间应足够长，以观察足够数量的事件，从而获得稳健的统计能力。较短的随访时间可能会错过关键事件，并导致不完整或有偏差的结论。为避免结果出现偏差，尽量减少在不同时间招募的参与者之间的事件风险差异也至关重要。
	</li>
</ol>

生存分析中的设计考虑因素

生存研究必须经过精心设计，以考虑这些假设。事件的明确定义、充足的随访时间和尽量减少审查偏差的策略至关重要。当这些因素得到良好管理时，生存模型可以为一系列学科的事件发生时间现象提供宝贵的见解。

生存分析是一种统计方法，用于评估事件发生前的时间。该方法在医学领域常用于分析预期寿命。

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 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, which means the full event time is not observed for some individuals. Censoring is not the same as missing data. It can happen when a participant leaves a study, when the study ends before the event occurs, or when another event happens first, such as death from an unrelated cause. In a heart disease study, a person who dies in an accident would be censored at the time of death.

Independent censoring is another important assumption. It means the reason for censoring should not be related to the chance of the event of interest. If people with severe symptoms are more likely to drop out, the survival estimate can become biased. Reliable survival analysis depends on keeping censoring separate from participant health status.

Some assumptions apply to specific models. The Cox proportional hazards model assumes that the hazard ratio between two individuals stays constant over time. For example, if one group has twice the risk of an event at the start of a study, that same risk ratio should hold throughout the study. Stationarity is also important. It means that changes in event probability over time should affect groups in a similar way unless the model includes those differences.

The event being measured must be clear and clinically important. If the event definition is vague, or if relapse is misclassified, the results can lose accuracy. The follow-up period also needs to be long enough to capture enough events for strong statistical power. Researchers should also reduce differences in event risk among people recruited at different times. When these design choices are handled well, survival models can give useful insight into time-to-event questions.

Survival Analysis Assumptions in Studies

introduction to survival analysis

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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...

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Kaplan-Meier Survival Curve Basics

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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 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

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...

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Types of Censoring in Survival Analysis

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survival tree

Survival Tree in Time-to-Event Analysis

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parametric survival analysis: weibull and exponential methods

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Modeling Survival Time with Weibull and Exponential

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key terms and concepts

Explore the key assumptions behind survival analysis, including censoring, proportional hazards, stationarity, and follow-up time in study design.

Survival Analysis Assumptions in Studies | 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 Analysis Assumptions in Studies

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