ABSTRACT
In clinical trials, the 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 the evaluation 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 the evaluation of safety and effectiveness of the test treatment under investigation. Once the study endpoints have been selected, the 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 determine the treatment effect. Moreover, different study endpoints may not translate one another although they may be highly correlated with one another. In this chapter, a therapeutic index function proposed by Filozof et al., which was developed based on a utility function to combine and utilize information collected from all study endpoints, is discussed. 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.
