Abstract
Background: To identify and assess via simulation the impact of COVID-19 pandemic on oncology trials and discuss potential mitigation strategies for study design, data collection, endpoints and analyses.
Methods: We simulated clinical trials to evaluate the COVID-19 impact on overall survival and progression-free survival. We evaluated survival in single-region trials with different proportions of impacted patients across treatment arms, and in multi-region randomized trials with different proportions of impacted patients across regions. We also assessed the impact on PFS when the missingness of disease assessment and censoring rules vary. Impact on the trial success and robustness of statistical inference was summarized.
Results: Without regional impact, the impact on OS analysis is minimal if proportions of impacted patients are similar across arms, however, if a larger proportion of treatment arm patients are impacted, trials may suffer substantial power loss and underestimate treatment effect size. For multi-region trials, if more treatment arm patients are enrolled from more severely impacted regions, trials also have poorer performance. For PFS analysis, the intent-to-treat rule performs well even when the treatment arm patients are more likely to miss disease assessments, while the consecutive-missing censoring rule may lead to poorer performance.
Conclusion: COVID-19 affects oncology trials. Simulations would be highly informative to Data Monitoring Committee in understanding the impact and making appropriate recommendations, upon which the sponsor could start planning potential remedies. We also recommend a decision tree for choosing the appropriate methods for PFS evaluation in the presence of missing disease assessments due to COVID-19.
Keywords: Analysis; COVID-19; Data collection; Oncology; Trial design.
【저자키워드】 COVID-19, Analysis, Data collection, oncology, Trial design, 【초록키워드】 Treatment, clinical trial, Trial, COVID-19 pandemic, randomized trial, clinical trials, Simulation, survival, Impact, Patient, Study design, Decision tree, assessment, disease, trials, recommendations, Data collection, endpoints, ARMS, oncology, Data monitoring committee, Endpoint, statistical inference, assessments, proportions, overall survival, Sponsor, treat, treatment arm, robustness, Affect, regions, statistical, missingness, Treatment effect size, enrolled, identify, evaluate, proportion, evaluated, analyses, impacted, intent-to-treat, 【제목키워드】 Impact, Clinical trial design, Data collection,