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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

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

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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...
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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
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Multiple Regression01:25

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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Assumptions of Survival Analysis01:15

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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Modeling repeated self-reported outcome data: A continuous-time longitudinal Item Response Theory model.

Cécile Proust-Lima1, Viviane Philipps1, Bastien Perrot2

  • 1Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR1219, F-33000 Bordeaux, France.

Methods (San Diego, Calif.)
|January 18, 2022
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Summary

This study introduces a longitudinal Item Response Theory (IRT) model to analyze health data collected over time, even with varying measurement schedules. The new model accurately captures individual patient trajectories and can assess item bias over time.

Keywords:
Item response theoryLatent process modelLongitudinal dataMeasurement invarianceMixed model

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Area of Science:

  • Health Sciences
  • Psychometrics
  • Longitudinal Data Analysis

Background:

  • Item Response Theory (IRT) models are increasingly used in health sciences to analyze latent constructs.
  • Traditional IRT methods often assume simultaneous measurements, which is unrealistic for longitudinal studies.
  • Handling varied observation times is crucial for accurate analysis of health-related constructs over time.

Purpose of the Study:

  • To develop a longitudinal IRT model that accommodates varying observation times across individuals and items.
  • To integrate IRT with mixed model theory for analyzing repeated item measures.
  • To apply the model to study depressive symptomatology trajectories in end-stage renal disease patients.

Main Methods:

  • Combined IRT methodology with mixed model theory to create a longitudinal IRT model.
  • Utilized a Graded Response Model for binary and ordinal items, defining the latent construct as a continuous-time latent process.
  • Employed Maximum Likelihood Estimation, with implementation available in the R package lcmm.

Main Results:

  • The proposed longitudinal IRT model effectively handles varying observation times in health science data.
  • Demonstrated the model's application in analyzing depressive symptomatology trajectories in the PREDIALA study.
  • Showcased the model's utility for assessing Differential Item Functioning and measurement invariance over time.

Conclusions:

  • The novel longitudinal IRT model provides a robust framework for analyzing item-level data with irregular measurement occasions.
  • This approach enhances the analysis of latent constructs in longitudinal health studies, offering more accurate insights into patient trajectories.
  • The method facilitates the detection of measurement biases and changes in item performance over time, crucial for clinical research.