Name: life tables and survival patterns
Uploaded: 2025-01-09T15:14:16.000Z
Duration: 1 min 22 s
Channel: JoVE

K12 Standards

Texas Essential Knowledge and Skills

High School

Physics

PHYS.1

PHYS.1.F

Life Tables

Una tabla de vida o una tabla de mortalidad representan los patrones de supervivencia de una población.
Consta de varias columnas, cada una de las cuales tiene un propósito práctico, comenzando con intervalos de tiempo en la primera columna.
La segunda columna enumera el número de individuos, pacientes o sujetos al comienzo del estudio, y la tercera columna enumera los que murieron durante el intervalo.
La cuarta columna indica el número de personas que faltan a los seguimientos, y la quinta columna muestra el número de personas que se retiraron con vida durante el intervalo.
En la sexta columna se presenta el número efectivo de participantes expuestos al riesgo objeto de estudio.
El conteo se considera efectivo porque se ajusta a los participantes que se retiraron vivos o se perdieron durante el seguimiento, asumiendo que los retiros ocurren de manera uniforme a lo largo del intervalo.
Por lo general, se estima que los retiros ocurren a mitad del intervalo, lo que lleva a una reducción a la mitad en el denominador para calcular los riesgos.
Las siguientes dos columnas son probabilidades condicionales de muerte y supervivencia. Por lo general, se calculan de la siguiente manera.
Por último, la última columna mide las tasas de supervivencia acumuladas en todos los intervalos.

tabla de vida

Una tabla de vida es una herramienta estadística que resume los patrones de mortalidad y supervivencia de una población, y proporciona información detallada sobre la probabilidad de supervivencia o muerte en diferentes intervalos de edad dentro de una cohorte. Al organizar los datos sobre las probabilidades de supervivencia y las tasas de mortalidad, las tablas de vida ofrecen una imagen clara de la dinámica de la población a lo largo del tiempo. Se utilizan ampliamente en demografía, salud pública, ciencia actuarial y ecología para analizar la esperanza de vida, diseñar intervenciones sanitarias, estimar los riesgos de los seguros y estudiar los patrones de supervivencia de las especies en varios ecosistemas. En general, una tabla de vida típica se divide en varias columnas clave:

<ol>
	<li>Intervalo de edad: representa rangos de edad específicos. Esta columna divide la población en intervalos distintos, como 0 a 5 años, 5 a 10 años, etc.</li>
	<li>Número de personas vivas: indica la cantidad de individuos que sobreviven al inicio de cada intervalo de edad. A partir de una cohorte hipotética (a menudo 100 000), esta columna muestra cuántas personas siguen vivas en cada intervalo de edad.</li>
	<li>Número de muertes: muestra el número de personas que mueren en cada intervalo. Se calcula restando el número de personas vivas al final de un intervalo del número de personas vivas al comienzo.</li>
	<li>Tasa de mortalidad: representa la probabilidad de morir durante el intervalo de edad para las personas vivas al comienzo del intervalo. Proporciona una probabilidad de muerte específica por edad.</li>
	<li>Probabilidad de supervivencia: representa la probabilidad de sobrevivir al intervalo de edad. Es el complemento de la tasa de mortalidad.</li>
	<li>Esperanza de vida: indica el promedio de años restantes que se espera que viva una persona de edad (x). Se calcula sumando los años-persona vividos por las personas vivas en cada grupo de edad posterior y dividiéndolo por el número de personas vivas a la edad (x).</li>
</ol>

Las tablas de mortalidad se pueden clasificar en tablas de cohorte, que registran un grupo de individuos nacidos durante el mismo período a lo largo de sus vidas, y tablas de período, que ofrecen una instantánea de las tasas de mortalidad de una población en un momento específico. Estas tablas son invaluables para comprender la salud de la población, pronosticar la esperanza de vida futura, calcular las primas de seguros de vida y comparar los patrones de supervivencia entre poblaciones o grupos. Por ejemplo, pueden destacar las diferencias en la esperanza de vida entre países o evaluar el impacto de intervenciones específicas en las tasas de supervivencia.

Tabla de vida

Una tabla de vida o una tabla de mortalidad representan los patrones de supervivencia de una población.
Se compone de varias columnas, cada una de las cuales tiene un propósito práctico, ...

Education

Educación

JoVE Core

Statistics

Estadísticas

Unit 1

Unidad 1

Survival Analysis

Análisis de supervivencia

KEY TERMS AND CONCEPTS

TÉRMINOS Y CONCEPTOS CLAVE

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 different ecosystems. They are also useful for comparing survival patterns across populations or groups, such as differences in life expectancy between countries.

A typical life table includes several important columns. Age interval lists the age range being measured, such as 0-5 years or 5-10 years. Number alive shows how many individuals are still alive at the start of each interval, often beginning with a hypothetical cohort of 100,000.

Other columns describe changes within each age range. Number of deaths shows how many individuals die during the interval. Mortality rate gives the probability of dying during that age period, and survival probability shows the chance of surviving it. Life expectancy tells the average number of years an individual of age x is expected to live, based on the person-years lived by those who remain alive in later age groups.

Life tables are commonly grouped into cohort life tables and period life tables. A cohort life table tracks a group of individuals born during the same period over their lifetimes. A period life table gives a snapshot of mortality rates for a population at a specific point in time. Together, these tables help researchers forecast future lifespans and assess the effect of specific interventions on survival rates.

Life Tables and Survival Patterns

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

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 life tables as tools for tracking survival, mortality rates, and life expectancy across age intervals in populations, public health, and ecology.

Life Tables and Survival Patterns | JoVE K12

statistics

JoVE Core Estadística presenta a los estudiantes los conceptos y las aplicaciones de la recopilación, análisis, presentación e interpretación de datos. Estas lecciones animadas proporcionan un entendimiento de la estadística a estudiantes de una amplia variedad de disciplinas. Además, los vídeos de científicos en acción muestran la aplicación de los conceptos en experimentos de investigación clásicos y originales, llevados a cabo hoy en día en laboratorios de ámbito internacional.

Chemistry and Physics

IPC.1

IPC.1.F

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PHYS.1.A

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Experimental Designs

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Data Collection by Experiments

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Scientific Method

Observation and Background Research
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The Scientific Method in Nursing Process

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PHYS.1.B

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PHYS.1.C

Common Lab Glassware and Uses

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Lab Techniques

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Scientific Measurement and Lab Skills

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Cleaning Glassware

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Proper Lab Notebook Keeping

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Making Solutions in the Laboratory

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PHYS.1.D

Units and Standards of Measurement

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International System of Units

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Base Quantities and Derived Quantities

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How Data are Classified: Numerical Data

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Quantitative Analysis

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Qualitative Analysis

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Collect quantitative data using the International System of Units (SI) and qualitative data as evidence

PHYS.1.E

Review and Preview

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Ogive Graph

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Histogram

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Relative Frequency Histogram

The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. ...

Scatter Plot

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Bar Graph

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Multiple Bar Graph

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Pareto Chart

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Pie Chart

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A life table or a mortality table represents the survival patterns of a population.
It comprises multiple columns, each serving a practical purpose, ...

Contingency Table

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Organize quantitative and qualitative data using bar charts, line graphs, scatter plots, data tables, labeled diagrams, and conceptual mathematical relationships

Basic Concept

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Develop and use models to represent phenomena, systems, processes, or solutions to engineering problems

IPC.1.G

Scientific and engineering practices. The student, for at least 40% of instructional time, asks questions, identifies problems, and plans and safely conducts classroom, laboratory, and field investigations to answer questions, explain phenomena, or design solutions using appropriate tools and models. The student is expected to:

Life Tables and Survival Patterns

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