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

Tablica trwania życia lub tablica śmiertelności przedstawia wzorce przeżycia populacji.
Składa się z wielu kolumn, z których każda służy celom praktycznym, zaczynając od interwałów czasowych w pierwszej kolumnie.
Druga kolumna zawiera liczbę osób, pacjentów lub uczestników na początku badania, a trzecia kolumna zawiera listę osób, które zmarły w tym okresie.
Czwarta kolumna wskazuje liczbę osób, które nie otrzymały dalszych informacji, a piąta kolumna pokazuje liczbę osób, które wycofały się przy życiu w tym okresie.
W szóstej kolumnie przedstawiono efektywną liczbę uczestników narażonych na badane ryzyko.
Liczba jest uważana za skuteczną, ponieważ dostosowuje się do uczestników, którzy zostali wycofani przy życiu lub zagubieni w celu obserwacji, przy założeniu, że wypłaty odbywają się równomiernie przez cały przedział.
Zazwyczaj szacuje się, że wypłaty następują w połowie interwału, co prowadzi do zmniejszenia o połowę mianownika do obliczania ryzyka.
Poniższe dwie kolumny to warunkowe prawdopodobieństwa śmierci i przeżycia. Zazwyczaj są one obliczane w następujący sposób.
Wreszcie ostatnia kolumna mierzy skumulowane wskaźniki przeżycia we wszystkich przedziałach.

tabela życia

Tablica trwania życia to narzędzie statystyczne, które podsumowuje wzorce śmiertelności i przeżycia populacji, zapewniając szczegółowy wgląd w prawdopodobieństwo przeżycia lub śmierci w różnych przedziałach wiekowych w obrębie kohorty. Poprzez organizowanie danych na temat prawdopodobieństwa przeżycia i wskaźników śmiertelności, tablice trwania życia oferują jasny obraz dynamiki populacji w czasie. Są one szeroko stosowane w demografii, ochronie zdrowia publicznego, naukach aktuarialnych i ekologii do analizowania oczekiwanej długości życia, projektowania interwencji zdrowotnych, szacowania ryzyka ubezpieczeniowego i badania wzorców przeżycia gatunków w różnych ekosystemach. Ogólnie rzecz biorąc, typowa tablica trwania życia jest podzielona na kilka kluczowych kolumn: <ol> <li>Przedział wiekowy: Reprezentuje określone przedziały wiekowe. Ta kolumna dzieli populację na odrębne przedziały, takie jak 0-5 lat, 5-10 lat itd.</li> <li>Liczba żywych: Wskazuje liczbę osób, które przeżyły do początku każdego przedziału wiekowego. Zaczynając od hipotetycznej kohorty (często 100 000), ta kolumna pokazuje, ile osób nadal żyje w każdym przedziale wiekowym.</li> <li>Liczba zgonów: Pokazuje liczbę osób, które umierają w każdym przedziale. Oblicza się ją, odejmując liczbę osób żywych na końcu przedziału od liczby osób żywych na początku.</li> <li>Współczynnik śmiertelności: Przedstawia prawdopodobieństwo śmierci w przedziale wiekowym dla osób żyjących na początku przedziału. Zapewnia prawdopodobieństwo śmierci w zależności od wieku.</li> <li>Prawdopodobieństwo przeżycia: Przedstawia prawdopodobieństwo przeżycia przedziału wiekowego. Jest to dopełnienie współczynnika śmiertelności.</li> <li>Długość życia: Wskazuje średnią liczbę pozostałych lat, które osoba w wieku (x) ma przeżyć. Oblicza się ją, sumując liczbę osobolat przeżytych przez osoby żyjące w każdej kolejnej grupie wiekowej i dzieląc przez liczbę osób żyjących w wieku (x).</li> </ol> Tabele trwania życia można podzielić na tablice trwania życia kohortowego, które śledzą grupę osób urodzonych w tym samym okresie w ciągu ich życia, oraz tablice trwania życia okresowego, które oferują migawkę wskaźników śmiertelności populacji w określonym momencie. Tabele te są nieocenione dla zrozumienia zdrowia populacji, prognozowania przyszłej długości życia, obliczania składek na ubezpieczenie na życie i porównywania wzorców przeżywalności w populacjach lub grupach. Na przykład mogą one uwypuklić różnice w oczekiwanej długości życia między krajami lub ocenić wpływ określonych interwencji na wskaźniki przeżywalności.

Tabela życia

Tablica trwania życia lub tablica śmiertelności przedstawia wzorce przeżycia populacji.
Składa się z wielu kolumn, z których każda służy praktycznemu celowi, ...

Education

JoVE Core

Statistics

Statystyka

Unit 1

Jednostka 1

Survival Analysis

Analiza przeżycia

KEY TERMS AND CONCEPTS

KLUCZOWE TERMINY I POJĘCIA

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 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

Chemistry and Physics

IPC.1

IPC.1.F

Observational Research

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Solving Problems in Physics

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Experimental Designs

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The Scientific Method in Nursing Process

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Observational Studies

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PHYS.1.B

guidelines-in-case-of-a-laboratory-emergency

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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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Use appropriate tools such as balances, ballistic carts or equivalent, batteries, computers, constant velocity cars, convex lenses, copper wire, discharge tubes with power supply (H, He, Ne, Ar), data acquisition probes and software, dynamics and force demonstration equipment, electrostatic generators, electrostatic kits, friction blocks, graph paper, graphing technology, hand-held visual spectroscopes, inclined planes, iron filings, lab masses, laser pointers, magnets, magnetic compasses, metric rulers, motion detectors, multimeters (current, voltage, resistance), optics bench, optics kit, photogates, plane mirrors, prisms, protractors, pulleys, resistors, rope or string, scientific calculators, stopwatches, springs, spring scales, switches, tuning forks, wave generators, or other equipment and materials that will produce the same results.

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

The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a ...

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct ...

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.
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Contingency Table

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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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