A Bayesian basket trial design that borrows information across strata based on the similarity between the posterior distributions of the response probability.
Bayesian predictive sample size determination
Jensen-Shannon divergence
basket trial
clinical trial design
interim analysis
Journal
Biometrical journal. Biometrische Zeitschrift
ISSN: 1521-4036
Titre abrégé: Biom J
Pays: Germany
ID NLM: 7708048
Informations de publication
Date de publication:
03 2020
03 2020
Historique:
received:
29
12
2018
revised:
29
08
2019
accepted:
01
09
2019
pubmed:
15
10
2019
medline:
12
1
2021
entrez:
15
10
2019
Statut:
ppublish
Résumé
Basket trials simultaneously evaluate the effect of one or more drugs on a defined biomarker, genetic alteration, or molecular target in a variety of disease subtypes, often called strata. A conventional approach for analyzing such trials is an independent analysis of each of the strata. This analysis is inefficient as it lacks the power to detect the effect of drugs in each stratum. To address these issues, various designs for basket trials have been proposed, centering on designs using Bayesian hierarchical models. In this article, we propose a novel Bayesian basket trial design that incorporates predictive sample size determination, early termination for inefficacy and efficacy, and the borrowing of information across strata. The borrowing of information is based on the similarity between the posterior distributions of the response probability. In general, Bayesian hierarchical models have many distributional assumptions along with multiple parameters. By contrast, our method has prior distributions for response probability and two parameters for similarity of distributions. The proposed design is easier to implement and less computationally demanding than other Bayesian basket designs. Through a simulation with various scenarios, our proposed design is compared with other designs including one that does not borrow information and one that uses a Bayesian hierarchical model.
Identifiants
pubmed: 31608505
doi: 10.1002/bimj.201800404
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
330-338Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Références
Berry, S. M., Carlin, B. P., Lee, J. J., & Muller, P. (2011). Bayesian adaptive methods for clinical trials. London, England: Chapman & Hall.
Chu, Y., & Yuan, Y. (2018a). A Bayesian basket trial design using a calibrated Bayesian hierarchical model. Clinical Trials, 15, 149-158.
Chu, Y., & Yuan, Y. (2018b). BLAST: Bayesian latent subgroup design for basket trials accounting for patient heterogeneity. Journal of the Royal Statistical Society: Series C (Applied Statistics), 67, 723-740.
Crooks, G. E. (2008). Inequalities between the Jenson-Shannon and Jeffreys divergences. Tech. Note 004. Retrieved from http://threeplusone.com/Crooks-inequality.pdf
Cunanan, K. M., Iasonos, A., Shen, R., Begg, C. B., & Gönen, M. (2017). An efficient basket trial design. Statistics in Medicine, 36, 1568-1579.
De Santis, F. (2006). Sample size determination for robust Bayesian analysis. Journal of the American Statistical Association, 101, 278-291.
Freidlin, B., & Korn, E. L. (2013). Borrowing information across subgroups in phase II trials: Is it useful? Clinical Cancer Research, 19, 1326-1334.
Fuglede, B., & Topsoe, F. (2004). Jensen-Shannon divergence and Hilbert space embedding (PDF). Proceedings International Symposium on Information Theory. 30. https://doi.org/10.1109/ISIT.2004.1365067.
Hyman, D. M., Puzanov, I., Subbiah, V., & Faris, J. E. (2015). Vemurafenib in multiple nonmelanoma cancers with BRAF V600 mutations. New England Journal of Medicine, 373, 726-736.
Hobbs, B. P., & Landin, R. (2018). Bayesian basket trial design with exchangeability monitoring. Statistics in Medicine, 37, 3557-3572.
Kumar, P., & Chhina, S. (2005). A symmetric information divergence measure of the Csiszár's f-divergence class and its bounds. Computers and Mathematics with Applications, 49, 575-588.
LeBlanc, M., Rankin, C., & Crowley, J. (2009). Multiple histology phase II trials. Clinical Cancer Research, 15, 4256-4262.
Liu, R., Liu, Z., Ghadessi, M., & Vonk, R. (2017). Increasing the efficiency of oncology basket trials using a Bayesian approach. Contemporary Clinical Trials, 66, 67-72.
Neuenschwander, B., Wandel, S., Roychoudhury, S., & Bailey, S. (2015). Robust exchangeability designs for early phase clinical trials with multiple strata. Pharmaceutical Statistics, 15, 123-134.
Sambucini, V. (2008). A Bayesian predictive two-stage design for phase II clinical trials. Statistics in Medicine, 27, 1199-1224.
Simon, R., Geyer, S., Subramanian, J., & Roychowdhury, S. (2016). The Bayesian basket design for genomic variant-driven phase II trials. Seminars in Oncology, 43, 13-18.
Spiegelhalter, D. J., Freedman, L. S., & Blackburn, P. R. (1986). Monitoring clinical trials: Conditional or predictive power? Controlled Clinical Trials, 7, 8-17.
Teramukai, S., Daimon, T., & Zohar, S. (2012). A Bayesian predictive sample size selection design for single-arm exploratory clinical trials. Statistics in Medicine, 31, 4243-4254.
Teramukai, S., Daimon, T., & Zohar, S. (2015). An extension of Bayesian predictive sample size selection designs for monitoring efficacy and safety. Statistics in Medicine, 34, 3029-3039.
Thall, P. F., Wathen, J. K., Bekele, B. N., Champlin, R. E., Baker, L. H., & Benjamin, R. S. (2003). Hierarchical Bayesian approaches to phase II trials in diseases with multiple subtypes. Statistics in Medicine, 22, 763-780.
Ventz, S., Barry, W. T., Parmigiani, G., & Trippa, L. (2017). Bayesian response-adaptive designs for basket trials. Biometrics, 73, 905-915.
Wong, F., & Gelfand, A. E. (2002). A simulation-based approach to Bayesian sample size determination for performance under a given model and for separating models. Statistical Science, 17, 193-208.