Welcome to Kristoffer Magnusson's blog about

R, STATISTICS, PSYCHOLOGY, OPEN SCIENCE, DATA VISUALIZATION

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.

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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.

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Confounded dose-response effects of treatment adherence: fitting Bayesian instrumental variable models using brms

Non-randomized comparisons are common in RCTs. In this post I show some examples of confounding and collider bias, using treatment adherence as an example. I present a small simulation study that show that common regression models used in clinical psychology, makes little sense, and that Bayesian instrumental variable regression can be easily fit using the R package brms.

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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.

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