Before You Launch Your Next Oncology Trial, Run the Simulation 

Oncology doesn’t leave much room for clinical trial design missteps. When survival can be measured in weeks or months, and endpoints are increasingly complex, the ability to plan thoroughly is critical. This context makes simulation a powerful tool for clinical trial planning. 

The Unique Challenges and Opportunities of Oncology Trials 

While every therapeutic area has its challenges and nuances, oncology presents a unique combination of factors that impact study design and execution: 

• Complex endpoints. Outcomes like progression-free survival (PFS), disease-free survival (DFS), and time to treatment failure require careful planning and interpretation. 
• Evolving data types. Oncology studies can incorporate biomarkers, genomics, and circulating tumor DNA, along with traditional clinical endpoints. 
• Innovative trial designs. Basket trials, umbrella studies, and adaptive designs are frequently used in oncology to accelerate timelines and improve trial flexibility. 
• Real-world integration. Companion diagnostics, external control arms, and real-world data sources are increasingly important in evaluating treatment efficacy. 

The constantly evolving nature of oncology research introduces operational and analytical complexities. These variables must be carefully accounted for during the design phase, which is where simulation proves its value. 

The Role of Simulation in Oncology Study Design  

Simulations are computer-generated models that mimic how a trial might perform under various scenarios. Rather than reacting to issues mid-study, simulations allow research teams to explore potential outcomes and optimize design decisions in advance. 

For oncology trials, simulations can help answer: 

• How large should the sample size be? 
• When should interim analyses occur, and how should data be interpreted at those time points? 
• What design strategies are best suited to manage uncertainty and adapt as the trial progresses?  

Simulations also help identify key design parameters such as optimal stratification strategies, realistic accrual timelines, and the potential impact of patient crossover or delayed treatment effects. This early modeling can reduce the likelihood of costly amendments, improve operational planning, and increase confidence among internal and external stakeholders. 

Rethinking Endpoints for Immunotherapies 

Immunotherapies have reshaped expectations in oncology, but they also introduce challenges in how endpoints are defined and interpreted. For example, pseudoprogression can lead to misclassification of patients using standard RECIST criteria. Simulations that incorporate immune-related RECIST (iRECIST) can better capture true response rates. 

In a recent real-world study, objective response rates for checkpoint inhibitors increased from 28.5% under RECIST to 34.1% under iRECIST.¹ This difference is meaningful during trial planning, especially when establishing statistical assumptions or identifying high-response subgroups. 

Other time-dependent endpoints, such as durable response rate (DRR) or immune-related PFS, can also be evaluated through simulation. These endpoints help show long-term clinical benefit and can be critical for regulatory strategy. 

Modeling Real-World Variability 

Effective simulation depends on thoughtful assumptions. Real-world data (RWD) helps make these assumptions more accurate and representative of actual patient populations. 

RWD can inform inclusion criteria, biomarker prevalence, prior treatment exposure, and variations in disease progression. It also supports the creation of virtual cohorts that better reflect the heterogeneity seen in oncology populations. These models allow teams to explore how study results might vary based on differences in tumor burden, demographics, and biomarker expression. 

External control arms, drawn from RWD or historical trials, are also gaining significant traction. Simulations help evaluate their feasibility, considering potential biases and the challenges of aligning assessment schedules, index dates, and standard of care treatments across different data sources. 

From Hypothesis to Decision-Making Framework 

In addition to its ability to improve endpoint selection and more accurately calculate sample size, simulations can also help to establish a structured, data-driven framework for decision-making throughout the trial. 

In oncology, adaptive trial designs, interim analyses, and early access programs all require well-defined rules for when to pivot, expand, or stop. Simulations help define these thresholds in advance. 

They also support alignment with regulatory expectations. When assumptions are validated through modeling, sponsors are better prepared to defend those assumptions. Scenario planning with best-case, worst-case, and most-likely outcomes helps anticipate potential questions from regulators and ensures your team is ready with data to support key milestones. 

Importantly, simulations can also highlight the difference between where uncertainties are acceptable and where they could compromise the trial. Not all unknowns need to be resolved before a trial begins, but simulations help clarify which ones are critical to understand before moving forward. 

Collaborating Across Functions 

Input from statisticians, clinical leads, regulatory experts, and data managers is key to ensuring that the simulation models are not only technically sound but also practical and aligned with study goals. 

Bringing these perspectives together helps refine trial assumptions, identify knowledge gaps, and define what data will be most important to collect and when. This cross-functional planning ultimately supports more robust design decisions and better execution. 

Looking Ahead 

In oncology trials, timelines are tight, data is complex, and all decisions carry significant weight. Simulations provide a framework for navigating uncertainty, optimizing design, and improving confidence in trial success. 

Sponsors who invest early in simulation and cross-functional planning are better positioned to avoid downstream delays, support regulatory engagement, and deliver high-quality data that can advance promising therapies. 

To explore these topics in more detail, click below to watch our webinar Design, Data, and Decisions in Oncology Trials. Click here

To discuss your next project with one of our expert statisticians or data experts, get in touch with our team here.