Related Experiment Video
Updated: Feb 19, 2026

Modeling Ascending Vaginal Infection, Preterm Birth, and Neonatal Morbidity in Mice
Published on: October 10, 2025
Modeling low birth weights using threshold regression: results for U.S. birth data
1Desautels Faculty of Management, McGill University, Montreal, QC, Canada H3A 1G5. george.whitmore@mcgill.ca
Abstract:
Babies born live under 2,500 g or with a gestational age under 37 weeks are often inadequately developed and have elevated risks of infant mortality, congenital malformations, mental retardation, and other physical and neurological impairments. In this paper, we model birth weight as a first hitting time (FHT) of a birthing boundary in a Wiener process representing fetal development. We associate the parameters of the process and boundary with covariates describing maternal characteristics and the birthing environment using a relatively new regression methodology called threshold regression. Two FHT models for birth weight are developed. One is a mixture model and the other a competing risks model. These models are tested in a case demonstration using a 4%-systematic sample of the more than four million live births in the United States in 2002. An extensive data set for these births was provided by the National Center for Health Statistics. The focus of this paper is on the conceptual framework, models and methodology. A full empirical study is deferred to a later occasion.
More Related Videos
Related Concept Videos
Modeling with Differential Equations
Regression Toward the Mean
Mechanistic Models: Compartment Models in Individual and Population Analysis
z Scores and Area Under the Curve
Truncation in Survival Analysis
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

