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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

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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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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.
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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
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Related Experiment Video

Updated: May 2, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Visualising and modelling changes in categorical variables in longitudinal studies.

Mark Jones1, Richard Hockey, Gita D Mishra

  • 1Centre for Longitudinal and Life Course Research, School of Population Health, University of Queensland, Brisbane, Australia. m.jones@sph.uq.edu.au.

BMC Medical Research Methodology
|March 1, 2014
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Summary
This summary is machine-generated.

New lasagne plots and statistical models visualize changes in categorical variables over time in longitudinal studies. These tools effectively estimate population-level trends and individual transitions in health behaviors like smoking.

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Health Behavior Research

Background:

  • Complex data patterns can be revealed through compelling graphical techniques.
  • Lasagne plots offer a novel visualization for changes in categorical variables over time.
  • Statistical models are proposed to estimate marginal and transitional probabilities.

Purpose of the Study:

  • To introduce and demonstrate a novel lasagne plot for visualizing longitudinal categorical data.
  • To propose statistical models for estimating marginal and transitional probabilities of categorical variables.
  • To analyze changes in health behaviors (smoking, BMI, physical activity) in a women's health study.

Main Methods:

  • Utilized stacked bar charts with color-coding to represent category distributions and individual trajectories over time.
  • Employed nominal logistic regression models suitable for ordinal and nominal categorical variables.
  • Analyzed longitudinal data on smoking status, body mass index (BMI), and physical activity level.

Main Results:

  • Marginal models indicated linear population-level increases in BMI and decreases in smoking and physical activity over time.
  • Individual smoking status was highly predictable, BMI moderately predictable, and physical activity virtually unpredictable based on previous states.
  • Model predictions showed good agreement with observed probabilities, indicating adequate goodness-of-fit.

Conclusions:

  • The lasagne plot serves as a simple visual tool for understanding categorical variable transitions in longitudinal studies.
  • The proposed statistical models facilitate formal testing and estimation of marginal and transitional distributions.
  • These methods offer valuable insights into longitudinal categorical data analysis for individual-level changes.