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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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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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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

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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 Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
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Quadratic Models01:23

Quadratic Models

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Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Identifying typical trajectories in longitudinal data: modelling strategies and interpretations.

Moritz Herle1,2, Nadia Micali2,3,4, Mohamed Abdulkadir3

  • 1Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.

European Journal of Epidemiology
|March 7, 2020
PubMed
Summary

This study reviews methods for identifying multiple developmental trajectories in longitudinal data, crucial for public health. It offers guidance on analyzing individual variations and their links to health outcomes.

Keywords:
ALSPACGrowth mixture modelsLatent class growth analysisLongitudinal latent class analysisMixed effects models

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Developmental Science

Background:

  • Longitudinal individual-level data on biological, behavioral, and social factors are increasingly available.
  • Current analysis often uses mixed-effects models, focusing on average trajectories and individual variations.
  • Public health research requires more nuanced modeling to identify multiple typical trajectories.

Purpose of the Study:

  • To provide a practical overview of methods for identifying latent trajectories in epidemiological applications.
  • To review three common methods: growth mixture models, latent class growth analysis, and longitudinal latent class analysis.
  • To offer recommendations for identifying, interpreting, and relating these trajectories to other variables.

Main Methods:

  • Review of existing statistical methods for trajectory analysis.
  • Comparative analysis of growth mixture models, latent class growth analysis, and longitudinal latent class analysis.
  • Application of methods to longitudinal data on childhood body mass index and fussy eating.

Main Results:

  • Identification of distinct developmental trajectories is feasible using the reviewed methods.
  • These methods allow for the examination of heterogeneity in developmental patterns.
  • Relationships between identified trajectories and explanatory variables/outcomes can be investigated.

Conclusions:

  • Advanced statistical methods enable the identification of multiple typical trajectories from longitudinal data.
  • Understanding these distinct trajectories is vital for targeted public health interventions.
  • Recommendations are provided for robust application in epidemiological studies.