Life Table
Life Table - Related Videos
01:22
Life Tables
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2025
A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
01:22
Applications of Life Tables
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2025
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
09:23
Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System
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Cited by 13 •
2017
Life tables allow quantification of the sources and rates of mortality in insect populations and contribute to understanding, predicting and manipulating population dynamics in agroecosystems. Methods for conducting and analyzing cohort-based life tables in the field for an insect with sessile immature life stages are presented.
02:42
Half-life of a Reaction
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2020
The half-life of a reaction (t1/2) is the time required for one-half of a given amount of reactant to be consumed. In each succeeding half-life, half of the remaining concentration of the reactant is consumed. For example, during the decomposition of hydrogen peroxide, during the first half-life (from 0.00 hours to 6.00 hours), the concentration of H2O2 decreases from 1.000 M to 0.500 M. During the second half-life (from 6.00 hours to 12.00 hours), the concentration decreases from 0.500 M to...
12:49
Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay
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Cited by 7 •
2015
Luminescent identification of functional elements in 3’ untranslated regions (3’UTRs) (3’LIFE) is a technique to identify functional regulation in 3’UTRs by miRNAs or other regulatory factors. This protocol utilizes high-throughput methodology such as 96-well transfection and luciferase assays to screen hundreds of putative interactions for functional repression.
FAQs
A cohort life table follows a defined group through successive intervals, so its estimates reflect the mortality experience observed as that group ages. A period life table instead applies age-specific mortality rates from a particular period to estimate survival and remaining life under those conditions. This distinction helps statisticians separate observed cohort experience from a period-based population summary.
The number alive and the number of deaths provide the observations from which a table organizes survival probabilities and expected remaining years. Together, these quantities show both how many individuals remain at each interval and when losses occur. Examining them jointly prevents life expectancy from being treated as the only outcome and supports a fuller statistical description of longevity.
Age-specific mortality rates determine how survival patterns change across intervals rather than treating mortality as constant for the entire population. Differences at particular ages can reveal when deaths are concentrated and can alter estimates of expected remaining years. In statistical comparisons, this detail helps identify whether populations differ in the timing of mortality, not merely in an overall longevity measure.
Construction begins by defining the population and successive age or time intervals, then organizing observations on individuals alive and deaths within those intervals. The analyst derives or records survival probabilities and expected remaining years, using observed cohort follow-up or age-specific mortality rates for a period estimate. Consistent interval definitions are essential for interpreting the resulting measures.
Public health analysts can use the resulting measures to examine survival patterns and identify how mortality is distributed across ages. Those results support planning by showing whether a population experiences earlier or later losses and by summarizing expected remaining years. The same statistical information can contribute to demographic analysis and population research when assessing longevity patterns.
To compare populations, statisticians can align their age or time intervals and examine corresponding survival, mortality, and expected-years measures. This approach shows whether differences arise from the number surviving, the timing of deaths, or the resulting longevity estimates. Comparisons are useful for evaluating changes over time and for studying how survival patterns differ among groups.