ABSTRACTIn clinical trials, selection of appropriate study endpoints is critical for an accurate and reliable evaluation of safety and effectiveness of a test treatment under investigation. In practice, however, there are usually multiple endpoints available for measurement of disease status and/or therapeutic effect of the test treatment under study. For example, in cancer clinical trials, overall survival, response rate, and/or time to disease progression are usually considered as primary clinical endpoints for evaluation of safety and effectiveness of the test treatment under investigation. Once the study endpoints have been selected, sample size required for achieving a desired power is then determined. It, however, should be noted that different study endpoints may result in different sample sizes. In practice, it is usually not clear which study endpoint can best inform the disease status and measure the treatment effect. Moreover, different study endpoints may not translate one another although they may be highly correlated one another. In this article, we intend to develop an innovative endpoint namely therapeutic index based on a utility function to combine and utilize information collected from all study endpoints. Statistical properties and performances of the proposed therapeutic index are evaluated theoretically. A numerical example concerning a cancer clinical trial is given to illustrate the use of the proposed therapeutic index. 相似文献
1. To investigate Genkwa Flos hepatotoxicity, a cell metabolomics strategy combined with serum pharmacology was performed on human HL-7702 liver cells in this study.
2. Firstly, cell viability and biochemical indicators were determined and the cell morphology was observed to confirm the cell injury and develop a cell hepatotoxicity model. Then, with the help of cell metabolomics based on UPLC-MS, the Genkwa Flos group samples were completely separated from the blank group samples in the score plots and seven upregulated as well as two down-regulated putative biomarkers in the loading plot were identified and confirmed. Besides, two signal molecules and four enzymes involved in biosynthesis pathway of lysophosphatidylcholine and the sphingosine kinase/sphingosine-1-phosphate pathway were determined to investigate the relationship between Genkwa Flos hepatotoxicity and these two classic pathways. Finally, the metabolic pathways related to specific biomarkers and two classic metabolic pathways were analyzed to explain the possible mechanism of Genkwa Flos hepatotoxicity.
3. Based on the results, lipid peroxidation and oxidative stress, phospholipase A2/lysophosphatidylcholine pathway, the disturbance of sphingosine-1-phosphate metabolic profile centered on sphingosine kinase/sphingosine-1-phosphate pathway and fatty acid metabolism might be critical participators in the progression of liver injury induced by Genkwa Flos. 相似文献