Statistical analysis
allergy
REGRESSION, ALLERGY, PEANUT ALLERGY
The primary endpoint of this pooled analysis was peanut allergy prevalence—defined on the basis of a positive oral food challenge (OFC) or sensitization and symptom history where food challenge was not done. Ninety‐one per cent of peanut allergy diagnoses were made on the basis of OFC. The primary analysis methodology for the pooled endpoint was a logistic regression model. Univariate and multivariable adjustments to the risk of peanut allergy were made and shown as estimated probabilities of allergy, risk differences between the randomized groups and relative risk reductions calculated from the univariate and multivariable‐adjusted predicted probabilities from the logistic regression models. We pre‐specified a number of subgroups for comparison and the raw‐unadjusted proportions are shown according to the different pre‐specified subgroups. All tests were two‐sided at the alpha = 0.05 level of significance. Pearson's chi‐squared tests were also used to replicate individual study results. When one or more cells had expected counts less than 5, Fisher's exact test was used. Individual and combined study proportions were displayed as bar charts with frequencies, proportions and Causal inference methods were implemented to adjust PP analyses for biases often present due to post‐randomization imbalances of the intervention's uptake relative to assignment. The average treatment effect (ATE), which estimates the average treatment effect among the entire population, assumes each participant is able to receive the intervention. Doubly robust methods were used to estimate the ATE and 95% bootstrapped confidence intervals by combining inverse probability weighting and regression adjustment using the AIPW option and the bootstrap statement in SAS Proc Causaltrt.Causal inference PP analyses were performed using SAS software version 9.4 (Proc Causaltrt).