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Updated: Jun 11, 2026

Diffuse Optical Spectroscopy for the Quantitative Assessment of Acute Ionizing Radiation Induced Skin Toxicity Using a Mouse Model

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Published on: May 27, 2016

Two-dimensional toxic dose and multivariate logistic regression, with application to decompression sickness.

Jialiang Li¹, Weng Kee Wong

¹Department of Statistics & Applied Probability, Duke-NUS Graduate Medical School, National University of Singapore, Singapore 117546. stalj@nus.edu.sg

Biostatistics (Oxford, England)

|July 7, 2010

Summary

This summary is machine-generated.

This study introduces a new statistical method for analyzing multiple dichotomous outcomes in toxicology, specifically for estimating toxic doses in decompression sickness (DCS) studies. The Gumbel-type logistic regression model helps determine risk factor ranges for a fixed DCS probability.

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Last Updated: Jun 11, 2026

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Published on: May 27, 2016

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Published on: August 28, 2019

Area of Science:

Toxicology and Biostatistics
Risk Assessment and Dose-Response Modeling

Background:

Multivariate statistical methods for continuous toxicological data are well-established.
Multivariate techniques for dichotomous response variables, crucial in many toxicological studies, are less developed.
Decompression sickness (DCS) studies often involve multiple, simultaneous dichotomous outcomes requiring advanced statistical analysis.

Purpose of the Study:

To develop and present a novel statistical methodology for analyzing multiple dichotomous toxicological outcomes.
To address the challenge of estimating a 2D toxic dose corresponding to a fixed risk for multiple DCS types.
To provide a robust method for risk factor analysis in toxicological experiments with binary endpoints.

Main Methods:

Proposed a Gumbel-type generalization of logistic regression tailored for multiple dichotomous variables.
Employed maximum likelihood estimation (MLE) to fit the proposed statistical model.
Demonstrated the application of the method using real-world data from a Wisconsin sheep DCS study.

Main Results:

Successfully fitted the Gumbel-type generalized logistic regression model to the toxicological data.
The fitted model enables the estimation of a 2D toxic dose based on a fixed probability of developing DCS.
The methodology provides a quantitative approach to understanding the relationship between risk factors and multiple adverse outcomes.

Conclusions:

The proposed Gumbel-type generalized logistic regression offers a viable statistical solution for analyzing multiple dichotomous toxicological endpoints.
This method advances the field of dose-response modeling for complex toxicological outcomes.
The approach is particularly useful for studies like DCS where multiple binary outcomes and risk factors are of interest.