We will use the data for all age groups (highlighted) and we simplify analysis by setting Ny = Np. Thus, the only numbers you need to get from the table are ny and n. an estimator , an implied estimator of vaccine efficacy, a confidence interval (of the 1.96SE sort) for , the implied confidence interval for e. This will get you reasonably close to numbers in the table, though not per- fect. If you are interested, try to use exact binomial inference. The Clopper- Pearson interval is a hardcoded command in Stata (command CI prop, exact binomial is the default) and MATLAB (complete "code" you need is to type [phat,pci]=binofit(30,131)) and implemented in numerous R functions. We will use the data for all age groups (highlighted) and we simplify analysis by setting Ny = Np. Thus, the only numbers you need to get from the table are ny and n. an estimator , an implied estimator of vaccine efficacy, a confidence interval (of the 1.96SE sort) for , the implied confidence interval for e. This will get you reasonably close to numbers in the table, though not per- fect. If you are interested, try to use exact binomial inference. The Clopper- Pearson interval is a hardcoded command in Stata (command CI prop, exact binomial is the default) and MATLAB (complete "code" you need is to type [phat,pci]=binofit(30,131)) and implemented in numerous R functions