## Articles with the statistics tag

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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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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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.

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My R package 'powerlmm' has now been update to version 0.3.0. It adds support for a more flexible effect size specifiation.

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This post contains the slides from a talk I gave recently at Stockholm University

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My R package 'powerlmm' has now been update to version 0.2.0. It contains several improvements, and new features.

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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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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.

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The visualization illustrates a Bayesian two-sample t test

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I've added more options to my NHST visualization

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