Hi, I'm
Kristoffer.
I'm a researcher in clinical psychology. I build interactive tools for understanding statistics, and write about quantitative methods and causal inference.
Interactive visualizations
All visualizations →
Writing
All posts →Why Adjusted Regression Coefficients Are Less Descriptive Than They Look
An interactive article about how regression adjustment works and the pitfalls of conditional associations in descriptive studies.
Introducing PowerLMM.js: Browser-based power analysis for longitudinal models with dropout
A new tool for power analysis of longitudinal linear mixed-effects models (LMMs), with support for missing data, random slopes, and non-inferiority and equivalence tests.
The Cohen's d app translation project
My Cohen's d app supports multiple languages, and any help translating the content into more languages is very welcome.
Why linear mixed-effects models are probably not the solution to your missing data problems
Linear mixed-effects models are often used for their ability to handle missing data using maximum likelihood estimation. In this post I will present a simple example of when the LMM fails, and illustrate two MNAR sensitivity analyses: the pattern-mixture method and the joint model.
Heterogeneous treatment effects and homogeneous outcome variances
Recently there has been a couple of meta-analyses investigating heterogeneous treatment effects by analyzing the ratio of the outcome variances in the treatment and control group. The argument made in these articles is that if individuals differ in their response, then observed variances in the treatment and control group in RCTs should differ. In this post I explore this argument and provide a counterargument.