Just use multilevel models for your pre/post RCT data

What? If you’d like to study the effectiveness of a clinical treatment, one of the simplest and most widely used approaches it to recruit participants from the target population, measure the outcome variable during a pre-treatment assessment, randomly assign participants into a control condition or an experimental treatment condition, treat the participants in the treatment condition, and measure the outcome variable again at the conclusion of treatment.

Example power analysis report, II

Preamble In an earlier post, I gave an example of what a power analysis report could look like for a multilevel model. At my day job, I was recently asked for a rush-job power analysis that required a multilevel model of a different kind and it seemed like a good opportunity to share another example.

Notes on the Bayesian cumulative probit

What/why? Prompted by a couple of my research projects, I’ve been fitting a lot of ordinal models, lately. Because of its nice interpretive properties, I’m fond of using the cumulative probit.

Use emmeans() to include 95% CIs around your lme4-based fitted lines

Scenario You’re an R (R Core Team, 2020) user and just fit a nice multilevel model to some grouped data and you’d like to showcase the results in a plot.

Got overdispersion? Try observation-level random effects with the Poisson-lognormal mixture

What? One of Tristan Mahr’s recent Twitter threads almost broke my brain. wait when people talk about treating overdispersion by using random effects, they sometimes put a random intercept on each row?

Example power analysis report

Context In one of my recent Twitter posts, I got pissy and complained about a vague power-analysis statement I saw while reviewing a manuscript submitted to a scientific journal.

Make ICC plots for your brms IRT models

Context Someone recently posted a thread on the Stan forums asking how one might make item-characteristic curve (ICC) and item-information curve (IIC) plots for an item-response theory (IRT) model fit with brms.

Don't forget your inits

tl;dr When your MCMC chains look a mess, you might have to manually set your initial values. If you’re a fancy pants, you can use a custom function.

Effect sizes for experimental trials analyzed with multilevel growth models: Two of two

Orientation This post is the second and final installment of a two-part series. In the first post, we explored how one might compute an effect size for two-group experimental data with only \(2\) time points.

Effect sizes for experimental trials analyzed with multilevel growth models: One of two

Background This post is the first installment of a two-part series. The impetus is a project at work. A colleague had longitudinal data for participants in two experimental groups, which they examined with a multilevel growth model of the kind we’ll explore in the next post.