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.
Special requirements for data collection, analysis, and reporting Oncology trials encompass various categories of special data including the integration of external data such as biomarkers, Polymerase chain reaction (PCR) results, or data about other concomitant active treatments since the tested drug is frequently a second line treatment. Besides, oncology trials can incorporate complex elements such as dose escalations and cycles of therapy and adaptive approaches which can demand additional expertise. Often, reporting out an oncology trial involves complex data derivations and analysis of time to event endpoints such as overall survival, progression-free survival, and time to progression.
Opportunities of standardization Taking a standardized approach to oncology data collection and analysis helps to streamline workflows, leading to better preparedness and efficiency for the clinical team in 3 types of common scenarios that arise: • Decision-making for safety or efficacy evaluations at interim analyses as described above • Supporting streamlined development pathways such as a fast track approval or priority review by regulators • Exploring promising new oncology indications by being able to pool data from previously conducted clinical trials more easily
How do we make standardization happen in practice
First of all, it is critical for the CRO working on an oncology trial data to be clear upfront about the data plan and come to an explicit agreement around expectations. What data is being collected; how does the data need to be summarized, when will it be exchanged, and what will be the data dissemination plan? A clear plan should also be put in place about the handling of special data. Such a strategy relies on collaboration between the data management and statistical analysis team to work together to define the data transfer specifications and then outline in the statistical analysis plan how the data will be transformed.
When planned carefully, the entire data value chain contains opportunities for standardization from endpoints definition (using response criteria guidelines, like Cheson for Leukemia, and RECIST for solid tumors), to the analysis (ADaM), via data collection (CDASH, SDTM).
Trial sponsors must carefully plan their data consolidation and analysis strategies not only in preparation for CDISC-compliant submissions, but to respond to market influences and evolving clinical partnership models.
By working closely with their CRO and ensuring an experienced team is in place with oncology-specific domain knowledge, sponsors can alleviate some of these data pain points and set themselves up for success.
Interested in learning more about practical steps to take when considering and implementing industry data standards? Click the button below to access our on-demand webinar 'Decisions for your next trial: When to adopt the CDISC data standard' with Cytel's Angelo Tinazzi.
Nicolas Rouillé is Senior Director, Statistical Programming at Cytel, and Eric Henniger is Executive Director Biostatistics. They work with customers to provide high quality statistical analysis and reporting solutions for their clinical trials.