Eli Lilly and Company, Indianapolis, IN 46285, USA.
Clin Trials. 2013;10(3):407-13. doi: 10.1177/1740774513478229. Epub 2013 Mar 7.
Drug development has become increasingly costly, lengthy, and risky. The call for better decision making in research and development has never been stronger. Analytic tools that utilize available data can inform decision makers of the risks and benefits of various decisions, which could lead to better and more informed decisions.
Through some real oncology examples, we will demonstrate how using available data to analytically evaluate probability of study success (PrSS) can lead to better decisions in clinical development.
The predictive power, or average conditional power, is used to quantify the PrSS. To calculate the probability, we follow a general two-step process: (1) use Bayesian modeling and appropriate assumptions to synthesize relevant data to derive the distribution of treatment effect and (2) evaluate the PrSS analytically or via trial simulation.
We applied the procedure to several compounds in our oncology pipeline. The analysis informed decision making where PrSS was an important factor to consider.
When modeling the treatment effect, we made certain assumptions, including how two drugs work together and exchangeable treatment effects across studies. Those assumptions are reasonable for our specific situations but may not generalize well.
From our experience, PrSS based on available data can help decision making in drug development, particularly the Go/No-Go decision after the proof of concept trial is completed. When applicable, we recommend this evaluation be regularly done in addition to the routine data analysis for clinical trials.
药物研发的成本、时间和风险日益增加。人们对研发中更好决策的需求从未如此强烈。利用现有数据的分析工具可以为决策者提供各种决策的风险和收益信息，从而做出更好、更明智的决策。
通过一些肿瘤学的实际案例，我们将展示如何利用现有数据对研究成功概率（PrSS）进行分析评估，从而在临床开发中做出更好的决策。
使用预测能力或平均条件概率来量化 PrSS。为了计算概率，我们遵循一个通用的两步流程：（1）使用贝叶斯建模和适当的假设来综合相关数据，以得出治疗效果的分布；（2）通过试验模拟或分析评估来评估 PrSS。
我们将该程序应用于我们肿瘤学管道中的几种化合物。该分析为决策提供了信息，其中 PrSS 是需要考虑的重要因素。
在对治疗效果进行建模时，我们做出了某些假设，包括两种药物如何协同作用以及研究之间可交换的治疗效果。这些假设对于我们的具体情况是合理的，但可能无法很好地推广。
根据我们的经验，基于现有数据的 PrSS 可以帮助药物研发中的决策制定，特别是在概念验证试验完成后的是去是留决策。在适用的情况下，我们建议除了临床试验的常规数据分析外，还应定期进行这种评估。
