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.

Conditional logistic models with brms: Rough draft.

Preamble After tremendous help from Henrik Singmann and Mattan Ben-Shachar, I finally have two (!) workflows for conditional logistic models with brms. These workflows are on track to make it into the next update of my ebook translation of Kruschke’s text (see here).

If you fit a model with multiply imputed data, you can still plot the line.

What? If you’re in the know, you know there are three major ways to handle missing data: full-information maximum likelihood, multiple imputation, and one-step full-luxury1 Bayesian imputation. If you’re a frequentist, you only have the first two options.

Sexy up your logistic regression model with logit dotplots

What When you fit a logistic regression model, there are a lot of ways to display the results. One of the least inspiring ways is to report a summary of the coefficients in prose or within a table.

One-step Bayesian imputation when you have dropout in your RCT

Preamble Suppose you’ve got data from a randomized controlled trial (RCT) where participants received either treatment or control. Further suppose you only collected data at two time points, pre- and post-treatment.

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.