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Abstract PurposeIt remains unknown to what degree thyroid hormone replacement therapy (THRT) during and initiation after pregnancy determines pregnancy outcomes. The present study primarily aimed to quantify the impact of THRT patterns (including trajectories) on gestational age, birth weight, and head circumference of infants. The secondary aim was to compare results of trajectory with traditional analysis. MethodsWe combined data from the Norwegian Mother, Father and Child Cohort Study (MoBa) to other Norwegian registry data and the Norwegian Environmental Biobank. The study population included 54 020 women enrolled in MoBa in 2005 to 2008. On the basis of prescription records, we classified women into nonhypothyroid (n = 51 390; reference group), THRT after delivery (n = 1397), or medicated (n = 1233) groups. Applying Group‐Based‐Trajectory Models (GBTMs), we determined THRT trajectories among women in the medicated group. Propensity score weighting linked multiple treatment groups to pregnancy outcomes. ResultsPatterns were identified among women using medication during (Decreasing‐Low, Increasing‐Medium, Constant‐Medium, and Constant‐High) and after pregnancy. Women in the Increasing‐Medium (adjusted Odds Ratio [aOR] = 1.69; 95% Confidence Interval [CI], 1.06‐2.73) and the THRT after delivery (aOR = 1.19; 95% CI, 1.01‐1.42) groups had increased risk of giving birth to an LGA infant. In the traditional analysis, only women in the THRT after delivery group showed increased risk for an LGA infant (aOR = 1.19; 95% CI, 1.00‐1.42). We found no other differential effect among the five THRT patterns on the other outcomes. ConclusionsWomen with THRT after delivery or late onset THRT treatment showed increased risk of LGA infants. 

Abstract This manuscript extends the definition of the Absolute Standardized Mean Difference (ASMD) for binary exposure (M = 2) to cases for M > 2 on multiple imputed data sets. The Maximal Maximized Standardized Difference (MMSD) and the Maximal Averaged Standardized Difference (MASD) were proposed. For different percentages, missing data were introduced in covariates in the simulated data based on the missing at random (MAR) assumption. We then investigate the performance of these two metric definitions using simulated data of full and imputed data sets. The performance of the MASD and the MMSD were validated by relating the balance metrics to estimation bias. The results show that there is an association between the balance metrics and bias. The proposed balance diagnostics seem therefore appropriate to assess balance for the generalized propensity score (GPS) under multiple imputation.