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Predicting eczema severity using serum biomarkers

This repository contains the code for the article by Hurault et al. (preprint), "Can serum biomarkers predict the outcome of systemic therapy for atopic dermatitis?".

The code is written in the R language for statistical computing and the models using the probabilistic programming language Stan.

File structure

The dataset used in this study is not available according to our data sharing agreement. During the analysis, the dataset is loaded from a proprietary package TanakaData which includes the raw files as well as data processing functions.

Utility functions used within the scripts are available in functions.R. In addition, we used functions from Guillem Hurault's personal package, HuraultMisc.

The Models folder contains the different Stan models developed in this project:

  • RW.stan: the random walk model, one of the reference model.
  • AR.stan: the autoregressive model, one of the reference model.
  • MixedAR.stan: the mixed effect autoregressive model, one of the reference model.
  • SSM.stan: the Bayesian state space model without covariates.
  • SSMX.stan: the Bayesian state space model with covariates (following a horseshoe prior).

The modelling workflow in separated into different scripts:

  • check_models.R: Conduct prior predictive checks and fake data check of the different models. This script is notably useful to simulate data that resembles the one we used.
  • fit_models.R: Fit the different models to real data, perform diagnostics and posterior predictive checks.
  • run_validation.R: Run the validation process (K-fold cross-validation and forward chaining).
  • check_performance.R: Analyse validation results.

License

This open source version of this project is licensed under the GPLv3 license, which can be seen in the LICENSE file.