Associations of antiretroviral therapy and comorbidities with neurocognitive outcomes in HIV-1-infected patients.

Objective: The aim of this study was to evaluate associations of antiretroviral therapy (ART) and comorbidities with neurocognitive impairments (NCIs) in ART-naive HIV-1-infected patients in clinical practice.

Design: A retrospective study was conducted in ART-naive patients with HIV-1 diagnosis between January 2009 and December 2013 in the United States.

Methods: The primary outcome was any NCI that included HIV-associated neurocognitive disorders (HAND), Alzheimer's disease, Parkinson's disease, multiple sclerosis, and other dementias.

Methodology to assess clinical liver safety data.

Analysis of liver safety data has to be multivariate by nature and needs to take into account time dependency of observations. Current standard tools for liver safety assessment such as summary tables, individual data listings, and narratives address these requirements to a limited extent only. Using graphics in the context of a systematic workflow including predefined graph templates is a valuable addition to standard instruments, helping to ensure completeness of evaluation, and supporting both hypothesis generation and testing.

A proposed modification to Hy's law and Edish criteria in oncology clinical trials using aggregated historical data.

Purpose: Identifying drug-induced liver injury is a critical task in drug development and postapproval real-world care. Severe liver injury is identified by the liver chemistry threshold of alanine aminotransferase (ALT) >3× upper limit of normal (ULN) and bilirubin >2× ULN, termed Hy's law by the Food and Drug Administration. These thresholds require discontinuation of the causative drug and are seldom exceeded in most patient populations.

Megavariate data analysis of mass spectrometric proteomics data using latent variable projection method.

There are many data mining techniques for processing and general learning of multivariate data. However, we believe the wavelet transformation and latent variable projection method are particularly useful for spectroscopic and chromatographic data. Projection based methods are designed to handle hugely multivariate nature of such data effectively.