Vogt-Koyanagi-Harada syndrome in the setting of COVID-19 infection.

To report a case of Vogt-Koyanagi-Harada disease (VKH) in a 27-year-old male 2 weeks proceeding COVID-19 infection onset. Severe complications of VKH can be avoided by early diagnosis and adequate treatment with corticosteroids and immunosuppressants. It is possible that COVID-19 was a potential immunological trigger of VKH in our patient.

R package PRIMsrc: Bump Hunting by Patient Rule Induction Method for Survival, Regression and Classification.

PRIMsrc is a novel implementation of a non-parametric bump hunting procedure, based on the Patient Rule Induction Method (PRIM), offering a unified treatment of outcome variables, including censored time-to-event (Survival), continuous (Regression) and discrete (Classification) responses. To fit the model, it uses a recursive peeling procedure with specific peeling criteria and stopping rules depending on the response. To validate the model, it provides an objective function based on prediction-error or other specific statistic, as well as two alternative cross-validation techniques, adapted to the task of decision-rule making and estimation in the three types of settings.

Cross-Validation of Survival Bump Hunting by Recursive Peeling Methods.

We introduce a survival/risk bump hunting framework to build a bump hunting model with a possibly censored time-to-event type of response and to validate model estimates. First, we describe the use of adequate survival peeling criteria to build a survival/risk bump hunting model based on recursive peeling methods. Our method called "Patient Recursive Survival Peeling" is a rule-induction method that makes use of specific peeling criteria such as hazard ratio or log-rank statistics.

R package MVR for Joint Adaptive Mean-Variance Regularization and Variance Stabilization.

We present an implementation in the R language for statistical computing of our recent non-parametric joint adaptive mean-variance regularization and variance stabilization procedure. The method is specifically suited for handling difficult problems posed by high-dimensional multivariate datasets ( ≫ paradigm), such as in 'omics'-type data, among which are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. The implementation offers a complete set of features including: (i) normalization and/or variance stabilization function, (ii) computation of mean-variance-regularized and statistics, (iii) generation of diverse diagnostic plots, (iv) synthetic and real 'omics' test datasets, (v) computationally efficient implementation, using C interfacing, and an option for parallel computing, (vi) manual and documentation on how to setup a cluster.

Regularized Variance Estimation and Variance Stabilization of High Dimensional Data.

Among the problems posed by high-dimensional datasets (so called ≫ paradigm) are that variable-specific estimators of variances are not reliable and tests statistics have low powers, both due to a lack of degrees of freedom. In addition, variance is observed to be a function of the mean. We introduce a non-parametric adaptive procedure that uses the information contained in the mean to generate shrinkage estimators of the mean and variance.