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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

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...
Longitudinal Research02:20

Longitudinal Research

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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...
Multiple Regression01:25

Multiple Regression

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...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...

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Related Experiment Video

Updated: Jul 6, 2026

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

[Mixed linear regression model for longitudinal data: application to an unbalanced anthropometric data set].

Maria Arlene Fausto1, Mariângela Carneiro, Carlos Mauricio de Figueiredo Antunes

  • 1Escola de Nutriçço, Universidade Federal de Ouro Preto, Ouro Preto, Brasil.

Cadernos De Saude Publica
|March 11, 2008
PubMed
Summary
This summary is machine-generated.

This study applied mixed-effects models to unbalanced longitudinal height data in infants born to HIV-infected mothers. Boys and infants who cleared HIV showed significantly greater height growth.

Related Experiment Videos

Last Updated: Jul 6, 2026

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

Area of Science:

  • Biostatistics
  • Pediatric Growth Monitoring
  • Epidemiology

Context:

  • Longitudinal data analysis in cohort studies often presents challenges with unbalanced datasets.
  • This research utilizes height measurements from infants born to HIV-infected mothers in Brazil.
  • The study addresses the specific complexities of analyzing pediatric growth data in this population.

Purpose:

  • To evaluate the applicability and effectiveness of mixed-effects models for analyzing unbalanced longitudinal height data.
  • To model and understand the growth patterns of infants exposed to HIV.
  • To estimate growth rates within different infant subgroups.

Summary:

  • Mixed-effects models were applied to unbalanced longitudinal height data from infants of HIV-infected mothers.
  • Significant height differences were observed: boys were taller than girls, and infants who cleared HIV (seroreverters) were taller than those remaining HIV-positive.
  • The model successfully described longitudinal height trends and estimated growth rates by gender and HIV status.

Impact:

  • Provides a robust statistical methodology for analyzing complex pediatric growth data.
  • Offers insights into the growth trajectories of infants affected by maternal HIV infection.
  • Contributes to understanding factors influencing child development in resource-limited settings.