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Related Concept Videos

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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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...
Life Tables01:22

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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,...
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Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
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Phylogenetic Trees03:21

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the...
Phylogenetic Trees03:21

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Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the...

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Tree Core Analysis with X-ray Computed Tomography
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Tree Core Analysis with X-ray Computed Tomography

Published on: September 22, 2023

Sampling-through-time in birth-death trees.

Tanja Stadler1

  • 1Institut für Integrative Biologie, ETH Zürich, Universitätsstr 16, 8092 Zürich, Switzerland. tanja.stadler@env.ethz.ch

Journal of Theoretical Biology
|September 21, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for analyzing evolutionary trees with incomplete sampling. The new density calculation is vital for understanding virus evolution and species diversification over time.

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Area of Science:

  • Evolutionary biology
  • Phylogenetics
  • Epidemiology

Background:

  • Analyzing evolutionary trees often involves incomplete sampling of extant and extinct individuals.
  • Time-sampled datasets are crucial in fields like virus epidemiology and phylogenetics.

Purpose of the Study:

  • To derive a tree density for constant rate birth-death processes with incomplete sampling.
  • To provide a tool for analyzing time-sampled phylogenetic datasets.

Main Methods:

  • Calculation of tree density for sampled extant and extinct individuals.
  • Application of the derived tree density in Bayesian phylogenetic inference.
  • Simulation of trees with specified numbers of sampled extant and extinct individuals.

Main Results:

  • A novel tree density formula accounting for incomplete sampling.
  • Demonstration of the density's utility in Bayesian phylogenetic reconstruction on a calendar timescale.
  • Methods for inferring birth and death rates from reconstructed trees.

Conclusions:

  • The derived tree density is a versatile tool for phylogenetic analysis.
  • This method enhances the study of evolutionary history using time-sampled data.
  • The approach is essential for hypothesis testing in evolutionary biology and epidemiology.