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Related Experiment Video

Updated: Jun 24, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Complementary Gompertz survival models: decreasing alive versus increasing dead.

Dexter M Easton¹

¹Department of Biological Science, Florida State University, Tallahassee, FL 32306-4370, USA. easton@bio.fsu.edu.

The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences

|March 27, 2009

Summary

Animal survival follows two Gompertz models: one with exponentially decreasing survivors and another with exponentially increasing deaths. These models explain diverse survival patterns in populations, influenced by factors like sex and genetics.

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Area of Science:

Ecology
Population Dynamics
Biostatistics

Background:

Survival patterns in animal populations are crucial for ecological understanding.
Classical Gompertz models describe survival but may not capture all observed patterns.
Existing models often assume a simple exponential decay in population size over time.

Purpose of the Study:

To introduce and validate an alternative Gompertz model for animal survival.
To differentiate between standard and alternative Gompertz survival curves.
To analyze factors influencing which survival model best fits a population.

Main Methods:

Mathematical modeling of two asymmetric sigmoid survival forms.
Comparison of standard Gompertz (decreasing alive) and alternative Gompertz (increasing dead) models.
Analysis of published survival data from various animal populations.

Main Results:

Identified two distinct animal survival patterns fitting asymmetric sigmoid curves.
The alternative Gompertz model accurately predicts "non-Gompertzian" survival plots.
Survival mode varied based on sex, genetic strain, nutrition, and activity levels.

Conclusions:

Animal survival can be modeled by either standard or alternative Gompertz functions.
The alternative model captures survival dynamics not explained by traditional assumptions.
Environmental and genetic factors significantly modulate population survival trajectories.