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Related Concept Videos

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...
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...
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...
Regression Analysis01:11

Regression Analysis

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:
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...

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Related Experiment Video

Updated: May 27, 2026

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

Ridge regression for longitudinal biomarker data.

Melissa Eliot1, Jane Ferguson, Muredach P Reilly

  • 1University of Massachusetts Amherst, USA.

The International Journal of Biostatistics
|November 4, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel mixed ridge estimator to analyze complex biomarker data over time. The method effectively handles correlated biomarkers and longitudinal data, improving disease progression analysis.

Keywords:
biomarkerscardiovascular disease (CVD)mixed effectsrepeated measuresridge regression

Related Experiment Videos

Last Updated: May 27, 2026

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

Area of Science:

  • Biostatistics
  • Biomarker Discovery
  • Longitudinal Data Analysis

Background:

  • Technological advances generate large biomarker datasets, posing analytical challenges due to high dimensionality and correlations.
  • Understanding disease progression requires analyzing time-dependent biomarker associations.
  • Existing methods struggle with correlated predictors and longitudinal data structures.

Purpose of the Study:

  • To develop a statistical method for analyzing high-dimensional, correlated biomarker data in longitudinal studies.
  • To address the challenge of time-dependent biomarker associations in disease progression.
  • To integrate ridge regression within a mixed-effects modeling framework.

Main Methods:

  • Proposed a mixed ridge estimator combining ridge regression and mixed-effects models.
  • Developed an expectation-maximization algorithm to estimate unknown variance and covariance parameters.
  • Validated the model using simulation studies and real-world data.

Main Results:

  • The mixed ridge estimator effectively accounts for correlations in predictor variables and repeated measures.
  • Demonstrated model performance in accurately characterizing biomarker responses over time.
  • Successfully applied the method to cardiometabolic biomarker data from an endotoxemia study.

Conclusions:

  • The mixed ridge estimator provides a robust approach for analyzing complex longitudinal biomarker data.
  • This method enhances the understanding of disease progression by integrating correlated biomarkers.
  • Offers a valuable tool for biomarker discovery and analysis in time-course studies.