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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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Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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Multiple Imputation for Longitudinal Data: A Tutorial.

Rushani Wijesuriya1,2, Margarita Moreno-Betancur1,2, John B Carlin1,2

  • 1Clinical Epidemiology & Biostatistics (CEBU), Murdoch Children's Research Institute, Parkville, Australia.

Statistics in Medicine
|January 23, 2025
PubMed
Summary
This summary is machine-generated.

Handling missing data in longitudinal studies requires accounting for individual clustering. Multiple imputation (MI) methods must align with analysis models, but current approaches are complex. This tutorial reviews accessible MI techniques for clustered longitudinal data.

Keywords:
clustered datafully conditional specificationjoint modelinglongitudinal datamissing datamultiple imputation

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Area of Science:

  • Medical Research Methodology
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Longitudinal studies collect repeated measures over time, necessitating analytical methods that account for correlated observations within individuals.
  • Missing data is a common challenge, particularly in longitudinal research where participant attrition can lead to incomplete datasets.
  • Multiple imputation (MI) is a standard technique for handling missing data, but requires careful consideration of the imputation model's compatibility with the analysis model.

Purpose of the Study:

  • To review existing Multiple Imputation (MI) approaches for incomplete longitudinal data, specifically addressing clustered individuals.
  • To highlight the importance of aligning the imputation model with the analysis model in longitudinal studies.
  • To provide practical guidance and reproducible code (R and Stata) for implementing these MI methods.

Main Methods:

  • Review of Multiple Imputation (MI) techniques suitable for longitudinal data with clustered individuals.
  • Discussion of imputation strategies, including treating repeated measures as distinct variables and using generalized linear mixed imputation models.
  • Illustrative examples using R and Stata code applied to a real-world case study.

Main Results:

  • Existing MI methods for longitudinal data, while effective, often involve complex data manipulation and advanced procedures, limiting their adoption.
  • The tutorial demonstrates that appropriate MI techniques can successfully handle missing data in clustered longitudinal settings.
  • Implementation guidance with code facilitates the application of these methods in medical research.

Conclusions:

  • Accurate analysis of longitudinal data requires imputation models that reflect the clustered nature of observations within individuals.
  • Despite implementation challenges, accessible MI methods exist for handling incomplete longitudinal data, improving research validity.
  • This work provides practical tools and a review to encourage the use of appropriate MI techniques in longitudinal medical research.