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

Анализ выживаемости, статистический метод, оценивает время до наступления события. Он обычно используется в медицине для анализа продолжительности жизни.
Крайне важно выбрать клинически значимое событие, которое является четко определенным, четким и наблюдаемым для точного анализа.
Одним из важнейших аспектов является цензура, которая происходит, когда данные неполны из-за таких событий, как смерть или выход участника из исследования. Например, у пациентов, покидающих исследование, данные подвергаются цензуре.
Независимая цензура означает, что причины цензуры — например, отказ от участия в исследовании — не связаны с результатом интересующего вас результата.
Далее, предположение о пропорциональных рисках Кокса предполагает, что относительный риск или отношения рисков между группами остаются постоянными.
Предположение о стационарности гарантирует, что вероятность изменения события с течением времени одинакова для всех исследовательских групп, если явно не смоделировано иное.
Кроме того, необходимо тщательно определить продолжительность последующего наблюдения и размер выборки, чтобы обеспечить достаточное количество событий для надежного анализа.

Предположения анализа выживаемости

Assumptions of Survival Analysis

Модели выживаемости анализируют время до наступления одного или нескольких событий, таких как смерть биологических организмов или отказ механических систем. Эти модели широко используются в таких областях, как медицина, биология, инженерия и здравоохранение, для изучения явлений времени до события. Чтобы обеспечить точность результатов, анализ выживаемости опирается на ключевые предположения и тщательный дизайн исследования. 

<ol> 
	<li>Время выживания имеет положительную асимметрию. Время выживания часто демонстрирует положительную асимметрию, в отличие от нормального распределения, предполагаемого во многих других анализах. Это означает, что события, как правило, происходят чаще на ранних этапах, с меньшим количеством событий по мере прогрессирования времени.</li> 

	<li>Цензурирование данных. Цензурирование происходит, когда полное время выживания для человека не наблюдается, но оно отличается от отсутствия данных. Распространенными причинами цензурирования являются отказ участников от исследования, окончание периода исследования до наступления события или участники, переживающие не связанные с ним события (например, смерть по не связанной с ним причине). Например, в исследовании сердечно-сосудистых заболеваний данные участника, который умирает в результате несчастного случая, будут подвергнуты цензуре в момент смерти.</li> 

	<li>Независимое цензурирование. Это предположение предполагает, что причины цензурирования не связаны с вероятностью интересующего события. Например, если участники с тяжелыми симптомами с большей вероятностью выбывают из исследования, оценки выживаемости могут стать смещенными. Обеспечение независимости цензурирования от состояния здоровья участников имеет решающее значение для надежного анализа.</li> 

	<li>Пропорциональные риски (специфичные для моделей Кокса). Модель пропорциональных рисков Кокса предполагает, что отношение рисков между любыми двумя людьми остается постоянным с течением времени. Например, если риск события в одной группе в два раза выше, чем в другой в начале исследования, это отношение рисков должно сохраняться в течение всего периода исследования.</li> 

	<li>Стационарность. Стационарность предполагает, что вероятность изменения события с течением времени делает это одинаково во всех группах, если явно не смоделировано. Например, при сравнении времени выживания между пациентами, получавшими новый препарат, и пациентами, получавшими стандартное лечение, внешние факторы, влияющие на выживание, должны одинаково влиять на обе группы, если они не учитываются.</li> 

	<li>Четкие и клинически важные события. Интересующее событие должно быть клинически значимым и четко определенным, чтобы обеспечить точное измерение и анализ. Неоднозначные или неправильно классифицированные события (например, неясные критерии рецидива) могут поставить под угрозу достоверность данных о времени выживания.</li> 

	<li>Адекватный период наблюдения. Длительность наблюдения должна быть достаточно большой, чтобы наблюдать достаточное количество событий для надежной статистической мощности. Короткие периоды наблюдения могут пропустить критические события и привести к неполным или предвзятым выводам. Также важно минимизировать различия в риске событий среди участников, набранных в разное время, чтобы избежать искаженных результатов.</li> 
</ol> 

Проектные соображения при анализе выживаемости 

Исследования выживаемости должны быть тщательно разработаны, чтобы учитывать эти предположения. Четкое определение события, достаточное время наблюдения и стратегии для минимизации смещения цензурирования имеют решающее значение. При грамотном управлении этими факторами модели выживаемости могут предоставить ценную информацию о явлениях времени до наступления события в различных дисциплинах.

Анализ выживаемости, статистический метод, оценивает время до наступления события. Он обычно используется в медицине для анализа продолжительности жизни.
Так и есть...

Education

JoVE Core

Statistics

Статистика

Unit 1

Блок 1

Survival Analysis

Анализ выживаемости

KEY TERMS AND CONCEPTS

ОСНОВНЫЕ ТЕРМИНЫ И ПОНЯТИЯ

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

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 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 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 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 Analysis Assumptions in Studies

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