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Related Concept Videos

Life Tables01:22

Life Tables

574
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,...
574
Applications of Life Tables01:22

Applications of Life Tables

394
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...
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Actuarial Approach01:20

Actuarial Approach

344
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
344
Life Histories01:29

Life Histories

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Overview
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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

677
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
677

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Related Experiment Video

Updated: Mar 6, 2026

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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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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Some examples of a method for determining key factors from life table data.

B F J Manly1

  • 1Biometrics Unit, University of Otago, P.O. Box 56, Dunedin, New Zealand.

Oecologia
|March 18, 2017
PubMed
Summary

Manly's (1977) method identifies key animal population life cycle factors. This approach is demonstrated using existing ecological data sets for validation.

Area of Science:

  • Ecology
  • Population Dynamics
  • Wildlife Biology

Background:

  • Understanding animal population dynamics is crucial for conservation.
  • Identifying key life cycle factors aids in predicting population trends and managing wildlife resources.

Purpose of the Study:

  • To illustrate the application of Manly's (1977) method for analyzing animal population life cycles.
  • To demonstrate the utility of this statistical approach on diverse published datasets.

Main Methods:

  • Application of Manly's (1977) statistical method.
  • Analysis of multiple published datasets on animal population dynamics.

Main Results:

  • The method effectively identifies critical factors influencing animal population dynamics.

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  • Demonstrated applicability across various species and ecological contexts.

    • Conclusions:

    • Manly's (1977) method provides a robust framework for dissecting animal population life cycles.
    • The findings support the method's value in ecological research and wildlife management.