Dose-escalation strategies which use subgroup information
- PMID: 29900666
- PMCID: PMC6175353
- DOI: 10.1002/pst.1860
Dose-escalation strategies which use subgroup information
Abstract
Dose-escalation trials commonly assume a homogeneous trial population to identify a single recommended dose of the experimental treatment for use in future trials. Wrongly assuming a homogeneous population can lead to a diluted treatment effect. Equally, exclusion of a subgroup that could in fact benefit from the treatment can cause a beneficial treatment effect to be missed. Accounting for a potential subgroup effect (ie, difference in reaction to the treatment between subgroups) in dose-escalation can increase the chance of finding the treatment to be efficacious in a larger patient population. A standard Bayesian model-based method of dose-escalation is extended to account for a subgroup effect by including covariates for subgroup membership in the dose-toxicity model. A stratified design performs well but uses available data inefficiently and makes no inferences concerning presence of a subgroup effect. A hypothesis test could potentially rectify this problem but the small sample sizes result in a low-powered test. As an alternative, the use of spike and slab priors for variable selection is proposed. This method continually assesses the presence of a subgroup effect, enabling efficient use of the available trial data throughout escalation and in identifying the recommended dose(s). A simulation study, based on real trial data, was conducted and this design was found to be both promising and feasible.
Keywords: Bayesian model-based method; dose-escalation; spike and slab; subgroup effect.
© 2018 The Authors. Pharmaceutical Statistics Published by John Wiley & Sons Ltd.
Figures


References
-
- Whitehead J, Williamson D. Bayesian decision procedures based on logistic regression models for dose‐finding studies. J Biopharm Stat. 1998;8(3):445‐467. - PubMed
-
- Rosenberger W, Haines L. Competing designs for phase I clinical trials: a review. Stat Med. 2002;21:2757‐2770. - PubMed
-
- Lièvre A, Bachet J‐B, Le Corre D, et al.. KRAS mutation status is predictive of response to Cetuximab therapy in colorectal cancer. Cancer Res. 2006;66(8):3992‐3995. - PubMed
-
- Chen C, Beckman R. Hypothesis testing in a confirmatory phase III trial with a possible subset effect. Stat Biopharm Res. 2009;1(4):431‐439.
-
- Temple R. Enrichment designs: Efficiency in development of cancer treatments. J Clin Oncol. 2005;23(22):4838‐4839. - PubMed
Publication types
MeSH terms
Grants and funding
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical
Research Materials