Accuracy of a mixed effects model interpolation technique for the estimation of pregnancy weight values

J Epidemiol Community Health. 2019 Aug;73(8):786-792. doi: 10.1136/jech-2018-211094. Epub 2019 May 31.


Background: Interpolation of missing weight values is sometimes used in studies of gestational weight gain, but the accuracy of these methods has not been established. Our objective was to assess the accuracy of estimated weight values obtained by interpolating from the nearest observed weight values and by linear and spline regression models when compared with measured weight values.

Methods: The study population included participants enrolled in the LIFECODES cohort at Brigham and Women's Hospital. We estimated weights at 28 (n=764) and 40 (n=382) weeks of gestation using participants' two nearest observed weights and subject-specific slopes and intercepts derived from repeated measures mixed effects models. In separate models, gestational age was parameterised as a linear and restricted cubic spline variable. Mean differences, absolute error measures and correlation coefficients comparing observed and estimated weights were calculated.

Results: Mean differences and mean absolute error for weights derived from the 28-week linear model (0.18 lbs (SD 6.92), 2.73 lbs (SD 6.35)) and 40-week linear model (-0.40 lbs (SD 5.43) and 2.84 lbs (SD 4.65)) were low. Mean differences were somewhat greater at 28 weeks for weight values derived from the nearest two observed values (mean difference -1.97 lbs (SD 8.74)) and from spline models (mean difference -2.25 lbs (SD 7.13)). Results were similar at 40 weeks.

Conclusions: Overall, weight values estimated using this interpolation approach showed good agreement with observed values. When repeated measures of weight are available, mixed effects models may be used to interpolate of missing weight values with minimal error.

Keywords: methods; missing data; pregnancy; weight.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Adult
  • Female
  • Humans
  • Models, Statistical
  • Pregnancy
  • Pregnancy Complications / epidemiology*
  • Pregnancy Trimesters*
  • Risk Factors
  • Weight Gain*