As we have highlighted in prior blog posts, the ability to augment design characteristics with custom R code is especially relevant to the ever-evolving therapeutic area of oncology. As regulatory guidelines are routinely adjusted to comply with clinical practice and current research, oncology study simulations often require specific analysis approaches and/or patient outcome data generation methods to conform to changing evidence thresholds and to create more realistic simulated scenarios.

Defining parameters and addressing uncertainty in survival studies

As in all clinical studies, there is a degree of uncertainty in assessing the treatment effect in trials employing a survival endpoint. For these types of studies, the timing of a patient’s event is typically sampled from a distribution with known parameters such as an exponential distribution with a median time value for each arm in the trial. The assumptions employed in defining these parameters are based on some prior knowledge derived from previous studies, meta-analyses, or other experience of the clinical development team.

Why does this matter?

When prior data is scarce, both the assumed distributions and median values are highly uncertain, and may lead to trials that are more costly, longer in duration, and/or with a diminished probability of success. It is therefore important for product development teams to derive meaningful values for these inputs in the design stage of clinical studies.

Custom R coding for oncology designs

One approach to derisking such trials is to simulate patient data based on a distribution of possible median time values for each arm rather than one single value. This accounts for the fact that the true value is difficult to estimate before the trial begins and removes the need to select just one value. This approach also provides confidence in additional investment based on more realistic assumptions.

To employ this design approach, we propose using flexible R code in conjunction with Cytel’s East Horizon^TM platform to customize the way in which the data for each simulated patient is generated. We propose modifying the response generation’s algorithm to consider a distribution of true treatment effects rather than one single value assumption. The probability of success becomes more conservative but also more informative as the simulation is more realistic of the trials about to take place. This gives the product development team more confidence in trial execution and a better estimation of trial costs and length.

Want to learn more?

Watch J. Kyle Wathen and Valeria Mazzanti’s webinar “A Closer Look at Assurance: Sampling Patient Outcomes from Prior Distributions to Account for Uncertainty in Response Scenarios”:

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