Faculty, Staff and Student Publications
Publication Date
7-19-2024
Journal
BMC Medical Research Methodology
Abstract
Background: New therapeutics in oncology have presented challenges to existing paradigms and trial designs in all phases of drug development. As a motivating example, we considered an ongoing phase II trial planned to evaluate the combination of a MET inhibitor and an anti-PD-L1 immunotherapy to treat advanced oesogastric carcinoma. The objective of the paper was to exemplify the planning of an adaptive phase II trial with novel anti-cancer agents, including prolonged observation windows and joint sequential evaluation of efficacy and toxicity.
Methods: We considered various candidate designs and computed decision rules assuming correlations between efficacy and toxicity. Simulations were conducted to evaluate the operating characteristics of all designs.
Results: Design approaches allowing continuous accrual, such as the time-to-event Bayesian Optimal Phase II design (TOP), showed good operating characteristics while ensuring a reduced trial duration. All designs were sensitive to the specification of the correlation between efficacy and toxicity during planning, but TOP can take that correlation into account more easily.
Conclusions: While specifying design working hypotheses requires caution, Bayesian approaches such as the TOP design had desirable operating characteristics and allowed incorporating concomittant information, such as toxicity data from concomitant observations in another relevant patient population (e.g., defined by mutational status).
Keywords
Humans, Bayes Theorem, Research Design, Clinical Trials, Phase II as Topic, Digestive System Neoplasms, Immunotherapy, Antineoplastic Agents, Computer Simulation, Phase II, Adaptive design, Bayesian, Oncology
DOI
10.1186/s12874-024-02278-3
PMID
39030498
PMCID
PMC11526600
PubMedCentral® Posted Date
7-19-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Genetic Phenomena Commons, Medical Genetics Commons, Oncology Commons