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Related Concept Videos

Development of Human Microbiota01:30

Development of Human Microbiota

The human microbiota begins developing at birth and undergoes continual change as we age. Infancy marks a critical period of microbial sensitivity, offering a “window of opportunity” during which beneficial microbes help mature the immune system. By age three, children typically develop a more stable and diverse microbial community. Newborns acquire microbes from their immediate environment; vaginal delivery favors maternal vaginal microbes, while cesarean births favor microbes from the skin...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

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Related Experiment Video

Updated: Jul 2, 2026

Microfluidic Model of Necrotizing Enterocolitis Incorporating Human Neonatal Intestinal Enteroids and a Dysbiotic Microbiome
06:51

Microfluidic Model of Necrotizing Enterocolitis Incorporating Human Neonatal Intestinal Enteroids and a Dysbiotic Microbiome

Published on: July 28, 2023

A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant

Brody Erlandson1, Ander Wilson1, Matthew D Koslovsky1

  • 1Department of Statistics, Colorado State University, 851 Oval Drive, Fort Collins, Colorado, 80524, United States.

Biostatistics (Oxford, England)
|July 1, 2026
PubMed
Summary
This summary is machine-generated.

Infant microbiome changes are linked to health risks. A new model captures time-varying exposures, improving understanding of infant gut microbial development and its health implications.

Keywords:
compositional datahigh-dimensionallongitudinal data analysismultivariate countstime-varying effects

Related Experiment Videos

Last Updated: Jul 2, 2026

Microfluidic Model of Necrotizing Enterocolitis Incorporating Human Neonatal Intestinal Enteroids and a Dysbiotic Microbiome
06:51

Microfluidic Model of Necrotizing Enterocolitis Incorporating Human Neonatal Intestinal Enteroids and a Dysbiotic Microbiome

Published on: July 28, 2023

Area of Science:

  • Microbiome research
  • Statistical modeling
  • Infant health

Background:

  • The infant microbiome rapidly changes and influences long-term health outcomes like allergies and asthma.
  • Modeling these temporal dynamics is crucial but challenging due to data complexity (compositionality, zero-inflation, repeated measures).
  • Existing models often assume constant effects and fail to capture time-varying exposures or the data's compositional nature.

Purpose of the Study:

  • To develop a novel statistical model for analyzing longitudinal infant microbiome data.
  • To account for time-varying covariate effects, zero-inflation, compositionality, and repeated measures in microbiome analysis.
  • To investigate the dynamic associations between infant exposures and microbiome composition.

Main Methods:

  • Proposed a functional concurrent zero-inflated Dirichlet-multinomial (DM) regression model.
  • The model handles repeated measures, time-varying relationships, zero-inflation, and the compositional structure of microbiome data.
  • Validated the model's accuracy and scalability through simulations.

Main Results:

  • The developed model accurately estimates underlying functional relations in microbiome data.
  • Simulations demonstrated the model's scalability to large compositional spaces.
  • Applied the model to analyze infant microbiome data during the first 11 weeks post-birth.

Conclusions:

  • The functional concurrent zero-inflated DM model effectively analyzes time-varying infant microbiome dynamics.
  • Alpha-diversity in infants is positively associated with higher gestational age and breast milk intake.
  • An accompanying R package and shiny app are available for implementing this advanced statistical method.