# Analysis: Statistical approaches

## Impact of treatment on 'rare event' incidence

*Notes from slack:*

> I’m finding some issues like this in analyzing rare events … not quite that rare, but still a few per thousand or a few per hundred.
>
> I’m taking 2 statistical approaches to the analysis (discussion, code, and data in links):

1. [Bayesian binomial-beta (a pretty standard setup I’m probably making overcomplicated)](https://daaronr.github.io/dualprocess/analysis-questions-and-tests.html#bayes_prop)
2. Randomization inference (simulation) … for a sort of [equivalence testing here](https://rethinkpriorities.github.io/methodology-statistics-design/inference-and-rough-equivalence-testing-with-binomial-outcomes.html#how-likely-are-proportions-this-similar-under-different-size-true-effect-sizes)

> I think either of these could be ‘flipped around’ to be used for power calculation or ‘the Bayesian equivalent of power calculation’

> My colleague Jamie Elsey has some expertise with the latter; [we’re putting together our discussion HERE](https://rethinkpriorities.github.io/methodology-statistics-design/power-workflow.html), although it’s mainly frequentist and not Bayesian ATM.

## Open and robust science: Preregistration and Preanalysis plans

There are reasons 'some pre-registration' or at least 'declaring your intentions in advance' is worth doing even if you aren't aiming at scientific publication

## Which statistical tests/methods

<https://gitlab.com/dsbowen/conditional-inference/-/blob/master/examples/bayes_primer.ipynb>

## [From Sample to Population: Multilevel Regression and Poststratification (MRP) and Survey Weighting](https://docs.google.com/document/d/14uTZqOpnKAK8_oRwgqlAhEXfyQPQUKK9/edit)

{% embed url="<https://docs.google.com/document/d/14uTZqOpnKAK8_oRwgqlAhEXfyQPQUKK9/edit>" %}


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