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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 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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Introduction To Survival Analysis01:18

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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.
The primary goal of survival analysis is to estimate survival time—the time...
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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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Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Comparing the Survival Analysis of Two or More Groups01:20

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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

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Initial data analysis for longitudinal studies to build a solid foundation for reproducible analysis.

Lara Lusa1,2, Cécile Proust-Lima3, Carsten O Schmidt4

  • 1Department of Mathematics, Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Capodistria, Slovenia.

Plos One
|May 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a systematic framework for initial data analysis (IDA) in longitudinal research. It enhances data screening to improve the reproducibility and validity of research findings from complex survey data.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Data Science

Background:

  • Reproducible research requires systematic initial data analysis (IDA) before addressing research questions.
  • Longitudinal studies present unique challenges for IDA due to repeated observations over time.
  • Existing IDA frameworks need adaptation for the complexities of longitudinal data.

Purpose of the Study:

  • To propose a systematic data screening framework for IDA in longitudinal studies.
  • To enhance the examination of data properties prior to planned statistical analyses.
  • To improve the reproducibility and validity of research using longitudinal data.

Main Methods:

  • Focused on the data screening component of IDA, assuming prior data cleaning and documented metadata.
  • Developed a five-type exploration approach: participation profiles, missing data, univariate/multivariate descriptions, and longitudinal aspects.
  • Illustrated the framework using hand grip strength data from a complex multi-wave survey.

Main Results:

  • Presented a detailed data screening plan for investigating age-associated decline in grip strength.
  • Provided reproducible R code for implementing the proposed IDA framework.
  • Demonstrated how the IDA report informs data analysts about data properties and analysis plan implications.

Conclusions:

  • The proposed systematic IDA framework enhances data screening for longitudinal studies.
  • The provided R code and checklist offer a practical tool for data analysts.
  • This approach supports informed decision-making, improving the reproducibility and validity of longitudinal research.