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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

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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...
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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...
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Updated: Dec 31, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Longitudinal dynamic functional regression.

Ana-Maria Staicu1, Md Nazmul Islam1, Raluca Dumitru2

  • 1North Carolina State University, Rayleigh, USA.

Journal of the Royal Statistical Society. Series C, Applied Statistics
|January 14, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible modeling framework for analyzing longitudinal data, accurately predicting outcomes from functional predictors over time. The approach enhances understanding of time-varying associations in various applications.

Keywords:
Functional dataFunctional principal component analysisLongitudinal functional regressionLongitudinal studyPenalizationTime-varying coefficient model

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Longitudinal studies involve repeated measurements over time, presenting unique statistical challenges.
  • Understanding time-varying associations between outcomes and functional predictors is crucial in many scientific fields.

Purpose of the Study:

  • To develop a parsimonious modeling framework for analyzing scalar outcomes and functional predictors in longitudinal studies.
  • To enable reconstruction of the full response trajectory for both Gaussian and non-Gaussian data.
  • To apply the methods to an animal science case study.

Main Methods:

  • Modeling time-varying functional predictors using orthogonal basis functions.
  • Expanding the time-varying regression coefficient using the same basis functions.
  • Applicable to both Gaussian and non-Gaussian response variables.

Main Results:

  • The proposed framework demonstrates excellent predictive accuracy.
  • The computational efficiency of the methods is superior to existing alternatives.
  • Successful application to an animal science dataset concerning sow feed intake and temperature.

Conclusions:

  • The developed modeling framework offers a robust and efficient approach for longitudinal data analysis.
  • The methods provide accurate predictions and insights into time-varying functional relationships.
  • The framework is versatile and applicable across various scientific domains.