(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

In statistical work, design and data analysis are often considered separately. Sometimes we do all sorts of modeling and planning in the design stage, only to analyze data using simple comparisons. Other times, we design our studies casually, even thoughtlessly, and then try to salvage what we can using elaborate data analyses.

It would be better to integrate design and analysis. My colleague Sebastian Weber works at Novartis (full disclosure: they have supported my research too), where they want to take some of the sophisticated multilevel modeling ideas that have been used in data analysis to combine information from different experiments, and apply these to the design of new trials.

Sebastian and his colleagues put together an R package wrapping some Stan functions so they can directly fit the hierarchical models they want to fit, using the prior information they have available, and evaluating their assumptions as they go.

Sebastian writes:

Novartis was so kind to grant permission to publish the RBesT (R Bayesian evidence synthesis Tools) R library on CRAN. It’s landed there two days ago. We [Sebastian Weber, Beat Neuenschwander, Heinz Schmidli, Baldur Magnusson, Yue Li, and Satrajit Roychoudhury] have invested a lot of effort into documenting (and testing) that thing properly. So if you follow our vignettes you get an in-depth tutorial into what, how and why we have crafted the library. The main goal is to reduce the sample size in our clinical trials. As such the library performs a meta-analytic-predictive (MAP) analysis using MCMC. Then that MAP prior is turned into a parametric representation, which we usually recommend to “robustify”. That means to add a non-informative mixture component which we put there to ensure that if things go wrong then we still get valid inferences. In fact, robustification is critical when we use this approach to extrapolate from adults to pediatrics. The reason to go parametric is that this makes it much easier to communicate that MAP prior. Moreover, we use conjugate representations such that the library performs operating characteristics with high-precision and high-speed (no more tables of type I error/power, but graphs!). So you see, RBesT does the job for you for the problem to forge a prior and then evaluate it before using it. This library is a huge help for our statisticians at Novartis to apply the robust MAP approach in clinical trials.

Here are the vignettes:

– Getting started with RBesT (binary)

– Using RBesT to reproduce Schmidli et al. “Robust MAP Priors”

The post Hey—here are some tools in R and Stan to designing more effective clinical trials! How cool is that? appeared first on Statistical Modeling, Causal Inference, and Social Science.

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