Methodological approaches to imputing early-pregnancy weight based on weight measures collected during pregnancy

Jiaxi Yang, Dongqing Wang, Anne Marie Darling, Enju Liu, Nandita Perumal, Wafaie W Fawzi, Molin Wang, Jiaxi Yang, Dongqing Wang, Anne Marie Darling, Enju Liu, Nandita Perumal, Wafaie W Fawzi, Molin Wang

Abstract

Background: Early pregnancy weights are needed to quantify gestational weight gain accurately. Different methods have been used in previous studies to impute early-pregnancy weights. However, no studies have systematically compared imputed weight accuracy across different imputation techniques. This study aimed to compare four methodological approaches to imputing early-pregnancy weight, using repeated measures of pregnancy weights collected from two pregnancy cohorts in Tanzania.

Methods: The mean gestational ages at enrollment were 17.8 weeks for Study I and 10.0 weeks for Study II. Given the gestational age distributions at enrollment, early-pregnancy weights were extrapolated for Study I and interpolated for Study II. The four imputation approaches included: (i) simple imputation based on the nearest measure, (ii) simple arithmetic imputation based on the nearest two measures, (iii) mixed-effects models, and (iv) marginal models with generalized estimating equations. For the mixed-effects model and the marginal model with generalized estimating equation methods, imputation accuracy was further compared across varying degrees of model flexibility by fitting splines and polynomial terms. Additional analyses included dropping third-trimester weights, adding covariate to the models, and log-transforming weight before imputation. Mean absolute error was used to quantify imputation accuracy.

Results: Study I included 1472 women with 6272 weight measures; Study II included 2131 individuals with 11,775 weight measures. Among the four imputation approaches, mixed-effects models had the highest accuracy (smallest mean absolute error: 1.99 kg and 1.60 kg for Studies I and II, respectively), while the other three approaches showed similar degrees of accuracy. Depending on the underlying data structure, allowing appropriate degree of model flexibility and dropping remote pregnancy weight measures may further improve the imputation performance.

Conclusions: Mixed-effects models had superior performance in imputing early-pregnancy weight compared to other commonly used strategies.

Trial registration: ClinicalTrials.gov NCT01119612 NCT01115478.

Keywords: Africa; Epidemiologic methods; Gestational weight; Pregnancy; Statistical model; Tanzania.

Conflict of interest statement

All authors declare they have no competing interests.

Figures

Fig. 1
Fig. 1
Imputed weights vs. observed weights (kg) of eight randomly selected subjects from Study I testing set based on the four different imputation methods (assigning the nearest weight measure, arithmetic imputation using the nearest two weight measures, mixed-effects model with the lowest mean absolute error, generalized estimating equation (GEE) model with the lowest mean absolute error), Dar es Salaam, Tanzania, 2010–2012
Fig. 2
Fig. 2
Imputed weights vs. observed weights (kg) of eight randomly selected subjects from Study II testing set based on the four different imputation methods (assigning the nearest weight measure, arithmetic imputation using the nearest two weight measures, mixed-effects model with the lowest mean absolute error, generalized estimating equation (GEE) model with the lowest mean absolute error), Dar es Salaam, Tanzania, 2010–2013

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