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Imputing Longitudinal Growth Data in International Pediatric Studies: Does CDC Reference Suffice?

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Summary
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This study developed a missing data imputation method for pediatric growth data in type 1 diabetes studies. The approach using CDC growth charts showed low imputation errors for height and weight.

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Area of Science:

  • Pediatric Endocrinology
  • Biostatistics
  • Data Science

Background:

  • Longitudinal growth data is crucial for pediatric studies, especially in type 1 diabetes (T1D).
  • Missing data in longitudinal studies can bias results and hinder accurate growth assessment.
  • Standardized imputation methods are needed for multi-country pediatric T1D cohorts.

Purpose of the Study:

  • To develop and evaluate a multiple imputation methodology for missing longitudinal growth data in pediatric T1D studies.
  • To assess the imputation accuracy using CDC reference data across diverse international cohorts.
  • To compare the performance of CDC reference data against country-specific growth charts.

Main Methods:

  • Analyzed a combined cohort of 23,201 subjects from five international T1D natural history studies.
  • Developed a multiple imputation technique leveraging LMS parameters from CDC growth reference data.
  • Quantified imputation errors using Mean Absolute Percentage Error (MAPE) and Normalized Root-Mean-Square Error (NRMSE).

Main Results:

  • The developed imputation method demonstrated low errors, with the largest MAPE for weight at 4.8% and height at 1.7%.
  • Imputation errors for height were consistently lower than for weight across all age groups.
  • No significant performance differences were observed when comparing CDC reference data to German or Swedish country-specific growth charts for height and weight.

Conclusions:

  • The proposed multiple imputation method using CDC reference data is effective for handling missing growth measurements in pediatric T1D studies.
  • CDC growth charts provide a reliable reference for imputation across different countries, simplifying multi-center data analysis.
  • This approach enhances the reliability of longitudinal growth analyses in international pediatric research.