Statistical analysis
SECONDARY
The analyses used an intent to treat (ITT) approach and included all eligible participants who were randomly assigned to a treatment arm. Balance in background clinical and demographic covariates was assessed across arms using The differences by treatment arm in secondary psychosocial outcomes were assessed using Linear Mixed Models (LMM), which upholds the ITT principle by using all available data. This method is comparable to ANCOVA with MI but provides more power, especially as the amount of missing data increases.A post hoc exploratory analysis was undertaken to determine and compare across arms the proportion of participants who received or scheduled a RRSO by 2 years post‐randomization and the incidence of received or scheduled RRSO between 1 and 2 years post‐randomization. For the latter calculation, only those who continued to be “at risk” were included in the analysis (i.e., those who had/scheduled a RRSO or who dropped out by year 1 were removed). Similar methods for modeling and dealing with missing data were used as for the primary outcome analysis.All analyses were undertaken using SAS (version 9.4; SAS Institute). To evaluate the primary outcomes, alpha was set at 0.05. For secondary outcomes, to accommodate multiple testing, the alpha was set to 0.005 using a Bonferroni correction.