Statistics
24 posts
Understanding Odds Ratios and Risk Ratios
An interactive article about odds ratios, risk ratios (relative risk), and risk differences, and why the interpretation of odds ratios is perhaps more odd than expected.
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.
Change over time is not "treatment response"
The term "treatment response" is both easy to understand and simultaneously often used when causal language is clearly unwarranted. In this post, I present a non-technical example of when a naïve subgroup analysis leads to the wrong conclusion that a subgroup of patients is treatment non-responders.
Estimating treatment effects and ICCs from (G)LMMs on the observed scale using Bayes, Part 1: lognormal models
In this post I show how to make marginal inferences on the untransformed scaled when using multilevel models with a non-linear transformation applied to the dependent variable (a log-transformation is used as an example). Cluster-specific versus population-average (conditional versus marginal) effects are compared using both average effects on the untransformed scale and using relative (multiplicative) effects.
Do you really need a multilevel model? A preview of powerlmm 0.4.0
The next version of powerlmm (0.4.0) will soon be released, besides bug fixes this version also includes several new simulation features. In this post I will show two examples that cover the major new features.
Power analysis for longitudinal multilevel models: powerlmm 0.3.0 is now out on CRAN
My R package 'powerlmm' has now been update to version 0.3.0. It adds support for a more flexible effect size specifiation.
Slides from my talk on how to do power analysis for longitudinal 2- and 3-level models.
This post contains the slides from a talk I gave recently at Stockholm University
Power analysis for longitudinal multilevel models: powerlmm 0.2.0 is now out on CRAN
My R package 'powerlmm' has now been update to version 0.2.0. It contains several improvements, and new features.
Introducing 'powerlmm' an R package for power calculations for longitudinal multilevel models
Over the summer I've been working on finishing my new R package 'powerlmm', which is now almost complete. It provides flexible power calculations for typical two- and three-level longitudinal linear mixed models, with unbalanced treatment groups and cluster sizes, as well as with missing data and random slopes at both the subject and cluster-level.
New d3.js visualization: Equivalence, non-inferiority and superiority testing
This visualization illustrates the appropriate tests to use when your research hypothesis is that two treatments are equally effective, or that a new treatment is no worse than the current gold standard.
New d3.js visualization: Bayesian inference
The visualization illustrates a Bayesian two-sample t test
[Updated] Statistical Power and Significance Testing Visualization
I've added more options to my NHST visualization
[Update] P-curve visualization updated with log x-axis
My p-curve visualization has been updated with the option to log the x-axis.
New d3.js visualization: the t-distribution and its normal approximation
This visualization shows how the t-distribution and the Gaussian (normal) distribution approaches each other when sample size increases.
Using R and lme/lmer to fit different two- and three-level longitudinal models
I often get asked about how to fit different longitudinal models in lme/lmer. In this post I cover several different two-level, three-level and partially nested models.
New visualization of the distribution of p-values using d3.js
Here's a new visualization that shows the p-curve distribution when comparing the means of two independent samples for varying effects. Many know that the distribution is uniform when the null is true, but what about when it isn't?
Interpreting Confidence Intervals – new d3.js visualization
Understand confidence intervals by using my new interactive visualization.
New d3.js visualization: Interpreting Correlations
This new visualization is an interactive display of the correlation between two variables.
New d3.js visualization: Understanding Significance Testing and Statistical Power
This new visualization is an interactive display of classical null hypothesis significance testing and statistical power.
Calculating the Overlap of Two Normal Distributions Using Monte Carlo Integration
In this post I show how to calculate the overlap of two normal distributions using simulations.
Visualizing a One-Way ANOVA using D3.js
A while ago I was playing around with the javascript package D3.js, and I began with this visualization—that I never really finished—of how a one-way ANOVA is calculated. I tried to make it look like a plot from ggplot2 except with interactive elements. Take a look at it after the jump
Using Ggplot2 to plot last.fm top 100 albums
I found out that last.fm had made data files available for their Best of 2011 artist list, and I thought it’d be a great opportunity to learn some more about data management in R and Ggplot2.