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 ...

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Mediation might be the ultimate example of how a method continues to be used despite a vast number of papers and textbooks describing the extremely strong assumptions required to estimate unbiased effects. My aim with this post is not to show some fancy method that could help reduce bias; rather ...

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This will be a non-technical post illustrating the problems with identifying treatment responders or non-responders using inappropriate within-group analyses. Specifically, I will show why it is pointless to try to identify a subgroup of non-responders using a naïve analysis of data from one treatment group only, even though we ...

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When a multilevel model includes either a non-linear transformation (such as the log-transformation) of the response variable, or of the expectations via a GLM link-function, then the interpretation of the results will be different compared to a standard Gaussian multilevel model; specifically, the estimates will be on a transformed scale ... [Read more...]

In this post I will show some of the new simulation features that will be available in powerlmm 0.4.0. You can already install the dev version from GitHub.
# GitHub
devtools::install_github("rpsychologist/powerlmm")
The revamped simulation functions offer 3 major new features:
Compare multiple model formulas, including OLS models (no random ... [Read more...]

My R package powerlmm 0.3.0 is now out on CRAN. It can be installed from CRAN https://cran.r-project.org/package=powerlmm or GitHub https://github.com/rpsychologist/powerlmm.
New features
This version adds support for raw effect sizes, and new standardized effect sizes using the function cohend(...). Here's an example ... [Read more...]

Here's the slides from a talk I gave recently at Stockholm University: "Power Analysis for Longitudinal 2- and 3-Level Models: Challenges and Some Solutions Using the R Package powerlmm".
The slides gives several code examples for a lot of powerlmm's f... [Read more...]

My R packge powerlmm 0.2.0 is now out on CRAN. It can be installed from CRAN https://cran.r-project.org/package=powerlmm or GitHub https://github.com/rpsychologist/powerlmm.
Changes in version 0.2.0
New features
Analytical power calculations now suppo... [Read more...]

Something that never ceases to amaze (depress) me, is how extremely common it is to see casual claims in RCTs, that are not part of the randomization. For instance, the relationship between treatment adherence and outcome, or between alliance and outcome, are often analyzed but seldom experimentally manipulated. This is ... [Read more...]

If you tend to do lots of large Monte Carlo simulations, you've probably already discovered the benefits of multi-core CPUs and parallel computation. A simulation that takes 4 weeks without parallelization, can easily be done in 1 week on a quad core laptop with parallelization. However, for even larger simulations reducing the ... [Read more...]

Over the years I've produced quite a lot of code for power calculations and simulations of different longitudinal linear mixed models. Over the summer I bundled together these calculations for the designs I most typically encounter into an R package. T... [Read more...]

I've received many emails regarding the percent of overlap reported in my Cohen's d visualization. Observant readers, have noted that I report a different number than Cohen (and other authors). For instance, if we open p. 22 in Cohen's Statistical power analysis for the behavior sciences, we see that Cohen writes ... [Read more...]

I've received many emails regarding the percent of overlap reported in my Cohen's d visualization. Observant readers, have noted that I report a different number than Cohen (and other authors). For instance, if we open p. 22 in Cohen's Statistical power analysis for the behavior sciences, we see that Cohen writes ... [Read more...]

My Statistical Power and Significance Testing Visualization now lets you vary effect size, sample size, power and significance level. There's also a new feature to rescale the plot and by clicking-and-dragging you can pan the visualization. [Read more...]

My p-curve tool now lets you show the x-axis on a log₁₀ scale, which makes it a lot easier to look at really small p-values. Thanks to Ged Ridgway for suggestion this!
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I often get asked how to fit different multilevel models (or individual growth models, hierarchical linear models or linear mixed-models, etc.) in R. In this guide I have compiled some of the more common and/or useful models (at least common in clinical psychology), and how to fit them using ... [Read more...]

I just published a new interactive visualization in my series of basic statistical concepts and techniques. This time I am trying to show how p-values are distributed. Check it out here: rpsychologist.com/d3/pdist/ [Read more...]

I just published a new interactive visualization in my series of basic statistical concepts and techniques. This time I have tried to explain confidence intervals for means. This visualization shows a simulation of repeated sampling from a normal dist... [Read more...]

Here is a new visualization done in d3js. In this visualization I show a scatter plot of two variables with a given correlation. The variables are samples from the standard normal distribution, which are then transformed to have a given correlation by... [Read more...]

The double-blinded placebo-controlled randomized trial have long been held as the gold standard in pharmacological research. Unfortunately, this design is impossible to mimic in clinical psychology. Even if we — at best — could try to keep the participants blinded to their treatment allocation, it would be rather hard to blind therapists ... [Read more...]

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