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Impact of COVID-19 pandemic on oncology clinical trial design, data collection and analysis.
Tang, Rui Sammi; Zhu, Jian; Chen, Tai-Tsang; Liu, Fang; Jiang, Xun; Huang, Bo; Lee, J Jack; Beckman, Robert A.
  • Tang RS; Servier Pharmaceuticals, Boston, MA, USA.
  • Zhu J; Servier Pharmaceuticals, Boston, MA, USA.
  • Chen TT; GlaxoSmithKline, Collegeville, PA, USA. Electronic address: tai-tsang.x.chen@gsk.com.
  • Liu F; Merck & Co., Inc., Kenilworth, NJ, USA.
  • Jiang X; Amgen Inc., Thousand Oaks, CA, USA.
  • Huang B; Pfizer Inc., Groton, CT, USA.
  • Lee JJ; The University of Texas MD, Anderson Cancer Center, TX, USA.
  • Beckman RA; Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA.
Contemp Clin Trials ; 116: 106736, 2022 05.
Article in English | MEDLINE | ID: covidwho-1750977
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 / Neoplasms Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Contemp Clin Trials Journal subject: Medicine / Therapeutics Year: 2022 Document Type: Article Affiliation country: J.cct.2022.106736

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 / Neoplasms Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Contemp Clin Trials Journal subject: Medicine / Therapeutics Year: 2022 Document Type: Article Affiliation country: J.cct.2022.106736