BACKGROUND: 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. PURPOSE: 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. METHODS: 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. RESULTS: We applied the procedure to several compounds in our oncology pipeline. The analysis informed decision making where PrSS was an important factor to consider. LIMITATIONS: 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. CONCLUSIONS: 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.
BACKGROUND: 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. PURPOSE: 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. METHODS: 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. RESULTS: We applied the procedure to several compounds in our oncology pipeline. The analysis informed decision making where PrSS was an important factor to consider. LIMITATIONS: 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. CONCLUSIONS: 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.
Authors: Joseph G Ibrahim; Ming-Hui Chen; Mani Lakshminarayanan; Guanghan F Liu; Joseph F Heyse Journal: Stat Med Date: 2014-10-23 Impact factor: 2.373
Authors: Annie Stylianou; Keona J H Blanks; Rachel A Gibson; Lindsay K Kendall; Mike English; Sarah Williams; Roshni Mehta; Andrew Clarke; Lynn Kanyuuru; Jalemba Aluvaala; Gary L Darmstadt Journal: J Glob Health Date: 2022-06-11 Impact factor: 7.664