Foetal ultrasound measurement imputations based on growth curves versus multiple imputation chained equation (MICE)

Abstract

Ultrasound measures are valuable for epidemiologic studies of risk factors for growth restriction. Longitudinal measurements enable investigation of rates of change and identification of windows where growth is impacted more acutely. However, missing data can be problematic in these studies, limiting sample size, ability to characterise windows of vulnerability, and in some instances creating bias. We sought to compare a parametric linear mixed model (LMM) approach to multiple imputation in this setting with multiple imputation by chained equation (MICE) methodology. Ultrasound scans performed for clinical purposes were abstracted from women in the LIFECODES birth cohort (n=1003) if they were close in time to three study visits (median 18, 26, and 35weeks' gestation). We created imputed datasets using LMM and MICE and calculated associations between demographic factors and ultrasound parameters cross-sectionally and longitudinally. Results were compared with a complete-case analysis. Most participants had ultrasounds at 18weeks' gestation, and ~50% had measurements at 26 and 35weeks; 100% had birthweight. Associations between demographic factors and ultrasound measures were similar in magnitude, but more precise, when either imputed datasets were used, compared with a complete-case analysis, in both the cross-sectional or longitudinal analyses. MICE, though ignoring the non-linear features of the trajectory and within subject correlation, is able to provide reasonable imputation of foetal growth data when compared to LMM. Because it simultaneously imputes missing covariate data and does not require specification of variance structure as in LMM, MICE may be preferable for imputation in this setting.