In 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.

在临床试验中，选择合适的研究终点对于准确可靠地评估所研究的治疗药物的安全性和有效性至关重要。然而，实际上，通常有多个终点可用于测量疾病状态和/或所研究测试疗法的治疗效果。例如，在癌症临床试验中，总体生存率，反应率和/或疾病进展时间通常被视为评估所研究测试治疗的安全性和有效性的主要临床终点。选择研究终点后，即可确定获得所需功效所需的样本量。但是，应注意，不同的研究终点可能会导致不同的样本量。在实践中，通常尚不清楚哪个研究终点能够最好地告知疾病状况并衡量治疗效果。此外，尽管不同的研究终点可能彼此高度相关，但它们可能不会彼此翻译。在本文中，我们打算开发一种创新的终点，即基于效用函数的治疗指数，以合并和利用从所有研究终点收集的信息。理论上评价了拟议治疗指标的统计性质和性能。给出了有关癌症临床试验的数值示例，以说明所提出的治疗指标的使用。我们打算开发一种创新的终点，即基于效用函数的治疗指标，以合并和利用从所有研究终点收集的信息。理论上评价了拟议治疗指标的统计性质和性能。给出了有关癌症临床试验的数值示例，以说明所提出的治疗指标的使用。我们打算开发一种创新的终点，即基于效用函数的治疗指标，以合并和利用从所有研究终点收集的信息。理论上评价了拟议治疗指标的统计性质和性能。给出了有关癌症临床试验的数值示例，以说明所提出的治疗指标的使用。