A Bayesian approach to benefit-risk assessment in clinical studies with longitudinal data.

Chuang-Stein et al. proposed a method for benefit-risk assessment by formulating a five-category multinomial random variable with the first four categories as a combination of benefit and risk, and the fifth category to include subjects who withdraw from study. In this article, we subdivide the single withdrawal category into four sub-categories to consider withdrawal for different reasons. To analyze eight-category data, we propose a two-level multivariate-Dirichlet Model to identify benefit-risk measures at the population level. For individual benefit-risk, we use a log-odds ratio model with Dirichlet process prior. Two methods are applied to a hypothetical clinical trial data for illustration.

pkDACLASS: Open source software for analyzing MALDI-TOF data.

UNLABELLED: In recent years, mass spectrometry has become one of the core technologies for high throughput proteomic profiling in biomedical research. However, reproducibility of the results using this technology was in question. It has been realized that sophisticated automatic signal processing algorithms using advanced statistical procedures are needed to analyze high resolution and high dimensional proteomic data, e.g., Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) data. In this paper we present a software package-pkDACLASS based on R which provides a complete data analysis solution for users of MALDITOF raw data. Complete data analysis comprises data preprocessing, monoisotopic peak detection through statistical model fitting and testing, alignment of the monoisotopic peaks for multiple samples and classification of the normal and diseased samples through the detected peaks. The software provides flexibility to the users to accomplish the complete and integrated analysis in one step or conduct analysis as a flexible platform and reveal the results at each and every step of the analysis. AVAILABILITY: The database is available for free at http://cran.r-project.org/web/packages/pkDACLASS/index.html.