## Articles with the simulation 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 summarize our recent paper on the consequences of ignoring therapist effects in longitudinal data analysis, which presents the results of a large simulation study.

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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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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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In this post I compare the performance of Amazon EC2 instances vs my HP workstation and my MacBook Pro, when doing Monte Carlo simulations.

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In this post I will use the theoretical and empirical sampling distribution of Cohen’s d to show the expected overestimation due to selective publishing. I will look at the overestimation for various sample sizes when the population effect is 0, 0.2, 0.5 and 0.8. The conclusion is that you should be weary of effect sizes from small samples, and that the issue is rather with type M (magnitude) errors than type I errors. At least is clinical psychology the pervasive problem is overestimation of effects and not falsely rejecting null hypothesis.

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Last week a group of Dutch scientists published a study providing further evidence of mindfulness’ ability to bolster creativity. Specifically they looked at if open awareness differed from focused attention in increasing divergent thinking

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In this post I use R to simulate the classic monty hall-problem and visualize the results with ggplot2.

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