Treatment effect size modelling
REGRESSION
All multivariate models were adjusted for the listed covariates identified in the prespecified SAP, except for (1) maternal BMI; and (2) second parent education level, for AEDC outcome models only.For each of the imputed datasets, the predicted probability of exposure or PS and associated inverse probability weight (IPW = 1/PS) were estimated using a logistic regression model, conditional on all analysis model covariates [For each imputed dataset, a doubly robust inverse-probability-weighted regression adjustment (IPWRA) model [Finally, estimates for each imputed dataset were pooled to provide overall ATE with associated 95% confidence limits using Rubin’s method.Provided the assumptions outlined above are satisfied, the estimates generated from these analyses can be interpreted as the population average causal effect, that is, the mean effect on the outcome if the treatment was applied to the entire population and contrasted with the outcome if the entire population received the control condition.