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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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Related Experiment Video

Updated: Aug 20, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Implementing Multiple Imputation for Missing Data in Longitudinal Studies When Models are Not Feasible: An Example

Chinchin Wang1,2, Tyrel Stokes3, Russell J Steele3

  • 1Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, H3T 1E2, Canada.

Clinical Epidemiology
|November 22, 2022
PubMed
Summary
This summary is machine-generated.

Random hot deck imputation offers a viable alternative to model-based methods for handling missing data in longitudinal studies when constraints cause implausible values. This method demonstrated stronger agreement for key variables compared to traditional approaches.

Keywords:
hot deck imputationlongitudinal studiesmissing at randommissing datamultiple imputationrandom hot deck imputation

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Model-based multiple imputation is common for missing at random data.
  • Data constraints can lead to implausible imputed values, rendering model-based methods infeasible.
  • Longitudinal studies often face challenges with missing data.

Purpose of the Study:

  • To illustrate the utility of random hot deck imputation for plausible multiple imputation in longitudinal studies.
  • To compare random hot deck imputation with model-based imputation methods.
  • To address situations where data constraints hinder standard imputation techniques.

Main Methods:

  • Utilized data from the Childhood Health, Activity, and Motor Performance School Study Denmark (CHAMPS-DK).
  • Created a gold-standard dataset and a synthetic dataset with missing values mimicking real-world patterns.
  • Employed random hot deck imputation by matching records and sampling based on generated probabilities.
  • Assessed variability and agreement (kappa) against the gold-standard dataset.

Main Results:

  • Random hot deck imputation showed moderate to strong agreement for activity frequency, sport, and sport counts (kappa ranges: 0.59–0.97).
  • Common model-based imputation methods yielded poor to moderate agreement (kappa ranges: 0.00–0.71).
  • Agreement improved with more available information and higher prevalence for binary variables.

Conclusions:

  • Random hot deck imputation is a recommended alternative when model-based imputation is infeasible due to covariate constraints.
  • This method provides plausible imputations for longitudinal data with complex constraints.
  • Consider random hot deck imputation for robust handling of missing data in challenging datasets.