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sciruby.com | ||
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matbesancon.xyz
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| | | | | Learning by doing: predicting the outcome. | |
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www.rdatagen.net
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| | | | | A key challenge - maybe the key challenge - of a stepped wedge clinical trial design is the threat of confounding by time. This is a cross-over design where the unit of randomization is a group or cluster, where each cluster begins in the control state and transitions to the intervention. It is the transition point that is randomized. Since outcomes could be changing over time regardless of the intervention, it is important to model the time trends when conducting the efficacy analysis. The question is how we choose to model time, and I am going to suggest that we might want to use a very flexible model, such as a cubic spline or a generalized additive model (GAM). | |
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www.fromthebottomoftheheap.net
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| | | | | [AI summary] The text discusses the use of generalized additive models (GAMs) to represent random effects as smooths, enabling the testing of random effects against a null of zero variance. It compares this approach with traditional mixed-effects models (e.g., lmer) and highlights the advantages and limitations of each. Key points include: (1) Representing random effects as smooths in GAMs allows for efficient testing of variance components and compatibility with complex distributional models. (2) While GAMs can fit such models, they are computationally slower for large datasets with many random effects due to the lack of sparse matrix optimization. (3) The AIC values for models with and without random effects are similar, suggesting that the simpler model i... | |
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distill.pub
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| | | What we'd like to find out about GANs that we don't know yet. | ||
