Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
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...
1.3K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.5K
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...
4.5K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

1.3K
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:
1.3K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

333
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...
333
Biostatistics: Overview01:20

Biostatistics: Overview

1.2K
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
1.2K
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

1.5K
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...
1.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Simultaneous Immunofluorescence-Based In Situ mRNA Expression and Protein Detection in Bone Marrow Biopsy Samples.

Bio-protocol·2026
Same author

Robust causal gene network estimation for large-scale single-cell perturbation screens using reduced control function.

bioRxiv : the preprint server for biology·2026
Same author

Development of an automated, imaging-based preoperative screening model for early identification of malnutrition in an abdominal surgery cohort.

medRxiv : the preprint server for health sciences·2026
Same author

Factors Associated with Adherence to Recommended Colorectal Surveillance Intervals in Lynch Syndrome.

Cancers·2026
Same author

Rejoinder to the discussion on "INTACT: A method for integration of longitudinal physical activity data from multiple sources".

Biometrics·2026
Same author

INTACT: a method for integration of longitudinal physical activity data from multiple sources.

Biometrics·2026
Same journal

Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention.

The annals of applied statistics·2026
Same journal

A NOVEL BAYESIAN FRAMEWORK UNCOVERING BRAIN CONNECTIVITY-TO-SHAPE RELATIONSHIP IN PRECLINICAL ALZHEIMER'S DISEASE.

The annals of applied statistics·2026
Same journal

EVALUATING MULTIPLEX DIAGNOSTIC TEST USING PARTIALLY ORDERED BAYES CLASSIFIER.

The annals of applied statistics·2026
Same journal

BRIDGING THE GAP: ENHANCING THE GENERALIZABILITY OF EPIGENETIC CLOCKS THROUGH TRANSFER LEARNING.

The annals of applied statistics·2026
Same journal

TREATMENT EFFECT HETEROGENEITY AND IMPORTANCE MEASURES FOR MULTIVARIATE CONTINUOUS TREATMENTS.

The annals of applied statistics·2026
Same journal

FEDERATED LEARNING OF ROBUST INDIVIDUALIZED DECISION RULES WITH APPLICATION TO HETEROGENEOUS MULTIHOSPITAL SEPSIS POPULATION.

The annals of applied statistics·2026
See all related articles

Related Experiment Video

Updated: May 5, 2026

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

17.4K

VARIABLE SELECTION FOR SPARSE DIRICHLET-MULTINOMIAL REGRESSION WITH AN APPLICATION TO MICROBIOME DATA ANALYSIS.

Jun Chen1, Hongzhe Li

  • 1Department of Biostatistics and Epidemiology, University of Pennsylvania chenjun@mail.med.upenn.edu.

The Annals of Applied Statistics
|December 7, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a sparse Dirichlet-multinomial (DM) regression model to identify associations between environmental factors and microbiome composition. The method effectively selects relevant covariates, improving microbiome data analysis.

Keywords:
Coordinate descentCounts dataOverdispersionRegularized likelihoodSparse group penalty

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.0K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.6K

Related Experiment Videos

Last Updated: May 5, 2026

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

17.4K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.0K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.6K

Area of Science:

  • Microbiology
  • Bioinformatics
  • Statistical Modeling

Background:

  • Next-generation sequencing enables detailed microbiome composition analysis.
  • Associating microbiome data with environmental factors is a key research goal.
  • Traditional models may lack power in high-dimensional microbiome studies.

Purpose of the Study:

  • To develop a robust statistical model for microbiome composition analysis.
  • To address challenges of high dimensionality and overdispersion in microbiome data.
  • To identify environmental covariates associated with bacterial taxa.

Main Methods:

  • Utilized a Dirichlet-multinomial (DM) regression model to handle overdispersion.
  • Developed a penalized likelihood approach with a sparse group Lasso penalty for variable selection.
  • Implemented an efficient block-coordinate descent algorithm for parameter estimation.

Main Results:

  • The sparse DM regression demonstrated superior identification of microbiome-associated covariates compared to existing methods.
  • The penalized approach effectively handles high-dimensional covariate data.
  • Nutrient intake was identified as a significant factor associated with human gut microbiome composition.

Conclusions:

  • The proposed sparse DM regression is a powerful tool for microbiome-environment association studies.
  • This method enhances the ability to detect relevant covariates and their impact on the microbiome.
  • Findings highlight the strong link between nutrient intake and human gut microbiome structure.