Foetal Ultrasound Measurement Imputations Based on Growth Curves Versus Multiple Imputation Chained Equation (MICE)

Details

• Personal Author:
  Cantonwine DE ; Ferguson KK ; McElrath TF ; Meeker JD ; Mukherjee B ; Yu Y
• Description:
  BACKGROUND: 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. METHODS: 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 35 weeks' 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. RESULTS: Most participants had ultrasounds at 18 weeks' gestation, and approximately 50% had measurements at 26 and 35 weeks; 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. CONCLUSIONS: 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. [Description provided by NIOSH]
• Subjects:
  Demography Epidemiology Occupational Health Pregnancy Risk Assessment Risk Factors Safety Women
• Keywords:
  Author Keywords: Imputation Demographic Characteristics Foetal Growth Humans Longitudinal Modelling MICE Risk Factors Statistical Methodology Ultrasound
• ISSN:
  0269-5022
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Massachusetts ; Michigan ; North Carolina ; OSHA Region 1 ; OSHA Region 4 ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  469-473
• Volume:
  32
• Issue:
  5
• NIOSHTIC Number:
  nn:20055309
• Citation:
  Paediatr Perinat Epidemiol 2018 Sep; 32(5):469-473
• Contact Point Address:
  Bhramar Mukherjee, Departments of Biostatistics and Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI
• Email:
  bhramar@umich.edu
• Federal Fiscal Year:
  2018
• Performing Organization:
  University of Michigan, Ann Arbor
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Paediatric and Perinatal Epidemiology
• End Date:
  20280630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:cb241f033349c9a5d067e06c6a9fba1b6155aca85d297325b1f87b8a29ed58279bed4866fb03cf6cb24f945ae1ffae090fbaa38ae2a542663a0ab547a9bea75c
• Download URL:
  https://stacks.cdc.gov/view/cdc/217645/cdc_217645_DS1.pdf
• File Type:
  [PDF - 418.00 KB ]