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Data and analysis in Modern Oncology Clinical Development

In the recent years, Oncology trials are seeing a technological shift that is expected to make them faster and more streamlined. Given the environment of dynamic care in oncology, clinical development too must be adaptable and dynamic. In a recent interview with Clinical Trials Arena, Caroline Morgan, Vice President of strategic consulting at Cytel, talks about the bespoke solutions that align clinical trial data with biometrics, and other new approaches that are helping to lead that change.

In oncology trials, data must be validated faster and with optimal quality to meet the demands of fast-paced programs. In such a scenario, companies cannot afford to use a siloed approach where clinical operations are kept separate from the statistical discussions that go into clinical trial design or the analysis of clinical data. A close collaboration is required between Data Management and Statistics groups to ensure quick and efficient presentation of clinical data to the regulators.

In oncology, the ability to design early phase trials to support combination therapies is extremely important. Several leading pharmaceutical companies are now incorporating Bayesian and adaptive methods, aiming to make their trials more flexible. This means that statisticians will want to analyse data more frequently. At Cytel, our statistical programmers have created apps in R Shiny to generate custom tables, statistics, and figures, that make data access more user-friendly.

To take advantage of the many benefits that adaptive trials offer, it is vital to address the various challenges involved. For example, as adaptive designs often require altering the trial’s parameters midstream, data management issues in these designs can be complex. Therefore, it is valuable to have experienced biostatisticians on hand who can help you preserve the trial’s integrity as well as interpret and report the findings correctly (Pallmann et al., 2018).

At Cytel, we have teams with extensive experience interacting with the regulators and designing innovative trials using synthetic control arms, head-to-head comparisons and related trial emulation methods. Each of these design types have different regulatory needs and translate to different data strategies throughout the data collection, storage and deployment journey.

To learn more about these new Oncology trial designs and their related data needs, read the interview.

Read Interview


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