Graphical modeling of binary data using the LASSO: a simulation study

We analyzed the Bolasso to derive graphical models in comparison with other LASSO based method. Model performance was assessed in a simulation study with random data generated via symmetric local logistic regression models and Gibbs sampling. Main outcome variables were the Structural Hamming Distance and the Youden Index.We applied the results of the simulation study to a real-life data with functioning data of patients having head and neck cancer.Bootstrap aggregating as incorporated in the Bolasso algorithm greatly improved the performance in higher sample sizes. The number of bootstraps did have minimal impact on performance. Bolasso performed reasonable well with a cutpoint of 0.90 and a small penalty term. Optimal prediction for Bolasso leads to very conservative models in comparison with AIC, BIC or cross-validated optimal penalty terms.Bootstrap aggregating may improve variable selection if the underlying selection process is not too unstable due to small sample size and if one is mainly interested in reducing the false discovery rate. We propose using the Bolasso for graphical modeling in large sample sizes.A common problem in contemporary biomedical research is the occurrence of a large number of variables that accompany relatively few observations. Thus, studying associations in high-dimensional data is not straightforward. Including all variables would result in a highly over parameterized model, computational complexity and unstable estimation of the associations [1]. This methodological problem has been solved for the domain of genomic medicine by using graphical modeling. Graphical models were identified as a promising new approach to modeling clinical data [2], and thereby the systems approach to health and disease.A promising approach to describe such complex relationships is graphical modeling. Graphical models [3] provide a probabilistic tool to display, analyze and visualize the net-like dependence structures by drawing a graph describing the con