Compositional data analysis method based on NMR metabolomics: An application to evaluate Chinese materia medica / 中草药

The peak area normalization is often needed as the preprocessing of NMR metabolomic data and the relative ratio of normalized data is the same as that of peak area in original spectrum, so the normalized data can reflect the relative information of peak area. This research selects the normalized data of the peak area for each sample to analyze, compositional data is the nonnegative vector containing only the relative information. Therefore the normalized data can be considered as compositional data. This paper used compositional data analysis based on NMR metabolomics to study the homogeneity for each group's samples, identify the characteristic metabolites which contribute the classification of different groups, and make the discriminate analysis for the given new sample in the evaluation of Chinese materia medica. In the case the analysis of quality evaluation on Astragali Radix, it can be seen from the results that the proposed method is feasible.

Sequential allocation to balance prognostic factors in a psychiatric clinical trial

OBJECTIVE: This paper aims to describe and discuss a minimization procedure specifically designed for a clinical trial that evaluates treatment efficacy for OCD patients. METHOD: Aitchison's compositional distance was used to calculate vectors for each possibility of allocation in a covariate adaptive method. Two different procedures were designed to allocate patients in small blocks or sequentially one-by-one. RESULTS: We present partial results of this allocation procedure as well as simulated data. In the clinical trial for which this procedure was developed, successful balancing between treatment arms was achieved. Separately, in an exploratory analysis, we found that if the arrival order of patients was altered, most patients were allocated to a different treatment arm than their original assignment. CONCLUSION: Our results show that the random arrival order of patients determine different assignments and therefore maintains the unpredictability of the allocation method. We conclude that our proposed procedure allows for the use of a large number of prognostic factors in a given allocation decision. Our method seems adequate for the design of the psychiatric trials used as models. Trial registrations are available at clinicaltrials.gov NCT00466609 and NCT00680602.