Interim futility and efficacy analysis
REGRESSION, MAY
For interim efficacy, we will use Bayesian posterior predictive probabilities to predict the final outcome of the trial based on interim results. For this predictive probability calculation, we will use a frequentist criterion: reject null hypothesis if final analysis 
Choose the neutral, enthusiastic, or skeptical prior for the treatment effect.Fit a Bayesian linear regression model to the primary outcome using the interim data. Include maternal treatment and stabilization dose as covariates.Calculate the posterior distributions for all regression terms.Use the interim data to calculate the observed distribution of maternal treatment.Use the interim data to calculate the observed distribution of the stabilization dose. May approximate with a normal distribution if suitable.Determine how many infants we still need to randomize in each arm. For each arm, generate data for the required number of hypothetical participants by doing the following for each required hypothetical participants:Sample a single value from each posterior distribution.Make a random draw from the observed distribution for maternal treatment.Make a random draw from the observed or approximated distribution for stabilization dose.Use the appropriate sampled values from steps 7a–7c to generate a hypothetical outcome. Use the data observed plus the hypothetical data generated in Step 7, above, to create a hypothetical complete trial and calculate the  Repeat Steps 7 and 8 many times. The PPoS is the proportion of hypothetical completed trials that achieve a 