Stan

The federal government recently granted emergency approval for the use of antibody rich blood plasma when treating hospitalized COVID-19 patients. This announcement is unfortunate, because we really don’t know if this promising treatment works. The best way to determine this, of course, is to conduct an experiment, though this approval makes this more challenging to do; with the general availability of convalescent plasma (CP), there may be resistance from patients and providers against participating in a randomized trial. [Read More]

This is essentially an addendum to the previous post where I simulated data from multiple RCTs to explore an analytic method to pool data across different studies. In that post, I used the nlme package to conduct a meta-analysis based on individual level data of 12 studies. Here, I am presenting an alternative hierarchical modeling approach that uses the Bayesian package rstan. Create the data set We’ll use the exact same data generating process as described in some detail in the previous post. [Read More]

I am currently wrestling with how to analyze data from a stepped-wedge designed cluster randomized trial. A few factors make this analysis particularly interesting. First, we want to allow for the possibility that between-period site-level correlation will decrease (or decay) over time. Second, there is possibly additional clustering at the patient level since individual outcomes will be measured repeatedly over time. And third, given that these outcomes are binary, there are no obvious software tools that can handle generalized linear models with this particular variance structure we want to model. [Read More]