Explore >> Select a destination
|
You are here 
|
fharrell.com | ||
| | | | |
www.rdatagen.net
|
|
| | | | | A researcher recently approached me for advice on a cluster-randomized trial he is developing. He is interested in testing the effectiveness of two interventions and wondered whether a 2×2 factorial design might be the best approach. As we discussed the interventions (I'll call them \(A\) and \(B\)), it became clear that \(A\) was the primary focus. Intervention \(B\) might enhance the effectiveness of \(A\), but on its own, \(B\) was not expected to have much impact. (It's also possible that \(A\) alone doesn't work, but once \(B\) is in place, the combination may reap benefits.) Given this, it didn't seem worthwhile to randomize clinics or providers to receive B alone. We agreed that a three-arm cluster-randomized trial-with (1) control, (2) \(A\) alone, a... | |
| | | | |
www.civilytics.com
|
|
| | | | | Update: Since this post was released I have co-authored an R package to make some of the items in this post easier to do. This package is called merTools and is available on CRAN and on GitHub. To read more about it, read my new post hereand check out the packageon GitHub. Introduction First of [...] | |
| | | | |
svmiller.com
|
|
| | | | | This is a companion blog post to how I typically teach students about statistical inference from a social (political) science perspective. | |
| | | | |
ouzor.github.io
|
|
| | | A minireview of R packages ggvis, rCharts, plotly and googleVis for interactive visualizations | ||
