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Modeling Fetal Weight for Gestational Age: A Comparison of a Flexible Multi-level Spline-based Model with Other Approaches

Author

Listed:
  • Villandré Luc

    (McGill University Health Centre)

  • Hutcheon Jennifer A

    (University of British Columbia)

  • Perez Trejo Maria Esther

    (McGill University)

  • Abenhaim Haim

    (McGill University)

  • Jacobsen Geir

    (Norwegian University of Science and Technology)

  • Platt Robert W

    (McGill University)

Abstract

We present a model for longitudinal measures of fetal weight as a function of gestational age. We use a linear mixed model, with a Box-Cox transformation of fetal weight values, and restricted cubic splines, in order to flexibly but parsimoniously model median fetal weight. We systematically compare our model to other proposed approaches. All proposed methods are shown to yield similar median estimates, as evidenced by overlapping pointwise confidence bands, except after 40 completed weeks, where our method seems to produce estimates more consistent with observed data. Sex-based stratification affects the estimates of the random effects variance-covariance structure, without significantly changing sex-specific fitted median values. We illustrate the benefits of including sex-gestational age interaction terms in the model over stratification. The comparison leads to the conclusion that the selection of a model for fetal weight for gestational age can be based on the specific goals and configuration of a given study without affecting the precision or value of median estimates for most gestational ages of interest.

Suggested Citation

  • Villandré Luc & Hutcheon Jennifer A & Perez Trejo Maria Esther & Abenhaim Haim & Jacobsen Geir & Platt Robert W, 2011. "Modeling Fetal Weight for Gestational Age: A Comparison of a Flexible Multi-level Spline-based Model with Other Approaches," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-26, August.
  • Handle: RePEc:bpj:ijbist:v:7:y:2011:i:1:n:32
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    References listed on IDEAS

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    1. Matthew J. Gurka & Lloyd J. Edwards & Keith E. Muller & Lawrence L. Kupper, 2006. "Extending the Box-Cox transformation to the linear mixed model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(2), pages 273-288.
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