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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

68
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
68
Multiple Regression01:25

Multiple Regression

3.1K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.1K
Regression Analysis01:11

Regression Analysis

5.8K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.8K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

88
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
88
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.5K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.5K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.4K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Evaluation of the United States' Permitless Concealed Carry Laws and Child Health in the ECHO Cohort.

Health and human rights·2026
Same author

Non-inclusive language in human subjects questionnaires: addressing racial, ethnic, heteronormative, and gender bias.

BMC public health·2025
Same author

Changes to Family Life, Youth COVID-19 Pandemic-Related Traumatic Stress, and the Youth Mental Health Crisis.

Journal of clinical child and adolescent psychology : the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53·2025
Same author

A Universal Duplex Sequencing Approach for Accurate Detection of Somatic Mutations.

bioRxiv : the preprint server for biology·2025
Same author

Public Water Arsenic and Birth Outcomes in the Environmental Influences on Child Health Outcomes Cohort.

JAMA network open·2025
Same author

Sociodemographic differences in parental hesitancy to the COVID-19 vaccine.

Vaccine·2025

Related Experiment Video

Updated: Jul 28, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

An adjusted partial least squares regression framework to utilize additional exposure information in environmental

Ruofei Du1,2, Li Luo1,2, Laurie G Hudson3

  • 1Biostatistics Shared Resource, University of New Mexico Comprehensive Cancer Center, Albuquerque, NM, USA.

Journal of Applied Statistics
|June 1, 2023
PubMed
Summary

This study introduces a new method to analyze environmental exposures and health outcomes, even with missing data. The approach improves the accuracy of identifying associations between environmental factors and specific health results.

Keywords:
Adjusted SIMPLSBirth CohortNavajometal mixture exposuremixture analysis

More Related Videos

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.1K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.4K

Related Experiment Videos

Last Updated: Jul 28, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.1K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.4K

Area of Science:

  • Environmental Health
  • Biostatistics
  • Population Studies

Background:

  • Large-scale environmental health studies often have incomplete outcome data for participants.
  • Existing regression methods, like partial least squares regression, require complete data for analysis.
  • This limits the full utilization of available exposure information.

Purpose of the Study:

  • To propose novel adjustments to partial least squares regression for analyzing environmental exposure-outcome associations.
  • To develop a framework that incorporates data from all participants, regardless of outcome measurement.
  • To improve the estimation and assessment of individual environmental exposure effects within complex mixtures.

Main Methods:

  • Modified partial least squares regression framework leveraging bilinear model structure.
  • Utilizing participants with and without outcome data for model fitting.
  • Estimating association effects of individual environmental exposures.

Main Results:

  • The proposed method allows all participants to contribute to model fitting, enhancing data utilization.
  • Incorporation of additional information leads to smaller root mean square errors in association effect estimation.
  • Improved ability to assess the statistical significance of exposure effects.

Conclusions:

  • The novel adjustments to partial least squares regression effectively address missing outcome data in environmental health studies.
  • This approach enhances the power and accuracy of identifying environmental exposure-outcome relationships.
  • The framework offers a more complete context for assessing environmental mixture exposures and their health impacts.