Introduction
This dashboard combines two powerful analytical methods on the same simulated clinical trial data. **Bayesian Inference** (MCMC) provides a precise, probabilistic framework for answering specific questions about a drug's effectiveness, while a **Variational Autoencoder** (VAE) offers an unsupervised, visual exploration of the data to uncover hidden patterns and relationships. By viewing both perspectives, we gain a comprehensive understanding of the trial's outcomes. In this simulation, the goal of the treatment is to **lower** blood pressure, so a more negative effect indicates better performance.
Confirmatory Analysis (Bayesian)
This section presents the results of a hierarchical Bayesian model, similar to what you'd get from a PyMC analysis. The table below provides the posterior summary for the effects of each treatment and demographic factor, including the mean and a credible interval. The probabilistic statements below offer direct, intuitive answers to key questions about the drug's performance.
| Parameter | Mean | SD | HDI 3% | HDI 97% |
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Exploratory Analysis (VAE)
This section shows the data organized in a 2-dimensional latent space, as learned by a VAE. The plot below visually confirms the relationships between treatment groups and demographic factors. Patients with similar profiles are clustered together, providing a compelling visual intuition of the drug's effects without relying on formal statistical tests. The tooltips show individual patient data, allowing you to explore the clusters more closely.