While perusing the news, I read eagerly about the CDC’s recent study examining associations between county-level mask mandates and COVID-19 growth rates. This study has already been the subject of angry retorts from the restaurant industry due to the CDC’s claim that restaurant closures reduced COVID-19 spread.
In this blog post, I use Gelman and Loken’s garden of forking paths analysis to construct a simulation showing why skepticism of AstraZeneca’s vaccine results is warranted at this early stage.
I start with Eric Novik’s excellent blog post on how to calculate the relevant statistics for the vaccine, i.e. vaccine efficacy (VE). This is defined as:
\[ VE = 1 - \frac{p_t}{p_c} \]
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We’ve all been in that seminar where the author puts up a slide containing regression coefficients, and buried in the bottom line of the table we can see little Ys and Ns indicating the kind of panel data model employed.
Background Conjoint survey experiments have become more popular in political science since the publication of Hainmueller, Hopkins and Yamamoto (2014). However, analysis of the statistical of power of conjoint experiments is difficult using standard parametric techniques because of the use of multiple treatments, interaction effects and paired vignettes.