By Nicolas Rouillé and Eric Henniger
The right design and the right data ultimately leads to the right decisions, so obtaining fit-for-purpose data, collected based on what your protocol is looking for is vital. However, there are several data pressure points facing oncology drug developers that need specialized expertise and processes to handle. In this blog, we run through some key aspects to consider to smooth your data collection and analysis.
Handling interim analyses
Many oncology trial designs incorporate interim analyses where data monitoring committees need to make fast, sound decisions based on the accumulating data about the future course of the trial. As with many aspects of clinical trials, proactive communication is key to success in the management of a DMC. Since formal efficacy interim analyses typically depend on the number of observed events, monitoring recruitment and event accrual is a crucial driver in the planning, and it can often be difficult for sponsors to establish when they are going to achieve enough events in order to have the DMC meeting. At Cytel, we have developed some probability algorithms to predict with high levels of certainty when we expect to reach a certain number of events, to help alleviate this challenge. Data cleaning and completeness are also crucial aspects since DMCs need to receive accurate data and analyses. The limitations of the analyses patterns of missing/incomplete data need to be communicated clearly and realistically to the DMC by the statistician.