Hi All,
I want to hijack this thread to present a study design that I am working on and to seek input from the community on the best analysis approach.
My study wants to analyze changes in some key variables after patients switch over from placebo to active treatments. This is essentially a pre-post single-arm design. We are defining ‘baseline’ (t0) as the time of switch and a 16 wk (t1) post switch. The variables that will be assessed are continuous and categorical.
My approach is to report changes in mean with confidence intervals for continuous variables and differences in proportions for binary outcomes.
For continuous outcomes, a paired t-test will be performed and a confidence interval will be generated by inverting the t-test. I am hoping there is an implementation in the BBR notes that seamlessly execute this step. 
For categorical variables summaries, I want to know if the prop.test() will yield the right statistic in the context of dependence as we will be using paired data. My checks from Agresti show that the difference in the proportion statistic and standard error derivation only make assumptions of independence on the outcome and not the predictor, so I was wondering if this is an appropriate test to report. Probably reporting McNemar will be the best approach but I do not know of a function in R that will invert and report the 95% CI for the differences with McNemar statistics.
Looking forward to other insights and suggestions.