🤖 AI Summary
Longitudinal anthropometric data are often challenging to integrate due to missing values and inconsistencies between multiple growth reference standards, such as those from the WHO and CDC. This study proposes a two-stage imputation method: first, linear interpolation is applied to fill missing values between observed measurements within individuals; second, remaining gaps are imputed using age- and sex-specific LMS growth models, with reference standards assigned according to each datum’s original source, thereby estimating individual percentiles. This approach uniquely embeds growth references explicitly into the imputation pipeline, enabling auditable, self-contained reconstruction that preserves data provenance. Evaluated on synthetic data with 30% missingness, the method achieved mean absolute errors of 1.78 kg (3.5%) for weight and 2.84 cm (2.0%) for height—negligible biases—and restored 100% data completeness.
📝 Abstract
Objective. Longitudinal datasets frequently contain missing weight and height measurements, and studies that combine data sources may index measurements against different growth reference standards (e.g., the WHO reference and CDC charts). We describe and evaluate a reproducible two-stage method that imputes missing anthropometry while making the choice of reference standard an explicit parameter. Methods. Stage 1 applies within-subject linear interpolation across visit dates (interior gaps only, no extrapolation). Stage 2 imputes remaining values from an age- and sex-specific growth reference using the LMS method by estimating each subject's centile, carrying it forward and backwards within the subject, defaulting to the 50th centile when a subject is never measured, and reading the expected value off the reference at the visit age. Different references can be supplied per data source so that the standard applied is recorded and auditable. We assessed recovery accuracy by masking and re-imputing a random 20% of observed values. All evaluations used computer-generated synthetic data. Results. On synthetic data (n = 60 subjects, 288 visits, 30% missing), the method resolved missingness to 100% completeness. Masked-value recovery gave a mean absolute error of 1.78 kg for weight (3.5% mean absolute percentage error) and 2.84 cm for height (2.0%), with negligible bias. Values recovered by within-subject interpolation were more accurate than those recovered from the growth reference, as expected, supporting the two-stage ordering. Conclusion. The method offers a simple, dependency-free, and auditable approach to anthropometric imputation, with explicit handling of differing reference standards and per-value provenance. Application to empirical data and propagation of imputation uncertainty into downstream models are the necessary next steps before use in substantive analyses.