As described before, the birth weights analyzed were corrected for gestational age and the sex of the child by using the birth weight standard deviation (SD) score.
Therefore, we used a variance component model [ 26] to predict the second-born's birth weight, using the first birth weight as a linear term for each stratum of gestational age of first- and second-born babies, with a common sex correction.
The 95% CIs of our observed effect size estimate in this group included the expected effect size, estimated using the birth weight smoking quantity and smoking quantity rs1051730 associations (Supplementary Material, Fig. S1).
Due to the oversampling of VLBW, LBW and HBW cases, the estimates were corrected by weighting using the variables "birth weight" and "preterm birth".
The heterogeneity in risk of neonatal death observed among babies within the same weight category provides further evidence that birth weight alone is an incomplete marker of fetal growth although, used alone, the birth weight ratio would be an even poorer predictor at the lower birth weights.
Infant weight was modeled as a time-varying covariate using the recorded birth weight and the interpolated weight at 6 and 16 months of age, as described above.
They showed that, comparisons using the "relative birth weight" scale, there were two strong and separable factors related to mortality: gestational age independent of birth weight, and relative birth weight at any given gestational age.
For example, using the estimated birth weight among South Asian babies in India, with 250 urban vs. 250 rural babies using a mean birth weight of 3.2 (0.65) kg we will have > 80% power to detect as small as a 0.22 kg difference between the cohorts.
Following [34], we test for the endogeneity of the adequacy of prenatal care use in the birth weight equation by testing for the statistical significance of these residuals in the equation.
Basic summary statistics are used to describe the birth weight and gestational age distributions.
