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

Longitudinal Studies01:26

Longitudinal Studies

305
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
305
Longitudinal Research02:20

Longitudinal Research

12.8K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
12.8K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

134
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...
134
Multiple Regression01:25

Multiple Regression

3.4K
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...
3.4K
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

976
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
976
One-Way ANOVA01:18

One-Way ANOVA

10.0K
One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
10.0K

You might also read

Related Articles

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

Sort by
Same author

Comparing Catheters to Fistulas in Older Patients Starting Hemodialysis (ACCESS HD).

Journal of the American Society of Nephrology : JASN·2026
Same author

Adjudicating Heart Failure Events in Participants Receiving Hemodialysis: Findings from Evaluation of Cinacalcet Hydrochloride Therapy to Lower Cardiovascular Events (EVOLVE) Trial.

American journal of nephrology·2026
Same author

Identification of Maintenance Dialysis Recipients in Administrative Health Data: A Systematic Review.

American journal of kidney diseases : the official journal of the National Kidney Foundation·2026
Same author

Patient and Healthcare Provider Priorities for Risk Prediction of Hospital Readmission: A Nominal Group Technique Consensus Study.

Journal of general internal medicine·2026
Same author

Identification of Maintenance Dialysis Recipients From an Electronic Clinical Information System and Effects on Estimates of Incidence and Prevalence.

Kidney medicine·2026
Same author

Environmental fluctuations alter the competitive trade-offs of group size in a social primate.

Nature ecology & evolution·2026
Same journal

Tracking Synthetic Adhesins on Bacterial Surfaces with Immunofluorescence Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Post-Selection Methods for Analyzing mRNA Display Selections and Optimization of Hits.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

High-Performance Computing in Tandem Mass Spectrometry (MS/MS) Peptide Identification.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Engineering and Adapting Disulfide-Containing Proteins to Enable Intracellular Functionality.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

AI-Driven Protein Research: From Prediction to Design.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Methods for the In Vitro Selection of Protein and Peptide Libraries Using mRNA Display.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Related Experiment Video

Updated: Nov 8, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.5K

Longitudinal Studies 2: Modeling Data Using Multivariate Analysis.

Pietro Ravani1, Brendan J Barrett2, Patrick S Parfrey2

  • 1Division of Nephrology, Department of Medicine, University of Calgary, Calgary, AB, Canada. pravani@ucalgary.ca.

Methods in Molecular Biology (Clifton, N.J.)
|April 19, 2021
PubMed
Summary
This summary is machine-generated.

Statistical models analyze exposure-disease links, adjusting for confounding factors to provide unbiased effect estimates and predictions. These models use systematic components for predictor effects and error components for precision measures like confidence intervals.

Keywords:
Confounding; interactionEffect estimatesEstimate precisionMultivariable analysisRegression methodsStatistical models

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.1K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K

Related Experiment Videos

Last Updated: Nov 8, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.5K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.1K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Research

Background:

  • Statistical models are crucial for understanding exposure-disease relationships.
  • Accounting for confounding factors is essential for accurate effect estimation.
  • Models provide unbiased estimates and aid in future outcome prediction.

Purpose of the Study:

  • To explain the structure and utility of statistical models in health research.
  • To highlight the role of adjustment in obtaining reliable effect estimates.
  • To describe the systematic and error components of statistical models.

Main Methods:

  • Utilizing statistical modeling to analyze exposure-outcome associations.
  • Incorporating adjustment for potential confounding variables.
  • Deconstructing models into systematic (predictor effects) and error (unexplained variability) components.

Main Results:

  • Adjustment in statistical models yields unbiased estimates of true effects.
  • Systematic components yield effect estimates (model coefficients).
  • Error components inform precision measures (Confidence Intervals).

Conclusions:

  • Statistical models are fundamental tools in epidemiological and health research.
  • Understanding model components enhances interpretation of results and precision.
  • Accurate modeling is key for reliable scientific inference and prediction.