Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Sep;17(5):414-436.
doi: 10.1002/pst.1860. Epub 2018 Jun 13.

Dose-escalation strategies which use subgroup information

Affiliations

Dose-escalation strategies which use subgroup information

Amy Cotterill et al. Pharm Stat. 2018 Sep.

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.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Example of a mixture prior on β composed of a normal slab and Dirac delta function spike
Figure A1
Figure A1
The dose‐toxicity curves used to generate data in additional Scenarios 7 to 11. Horizontal lines are references at P(DLT|d)=0.16 and 0.35. The solid black curve on each plot represents that of the biomarker negative subgroup in all scenarios. The dose‐toxicity curves for the biomarker positive group in these scenarios are shown for Scenarios 7 to 11 by the dashed red, green, dark blue, light blue and purple curves, respectively

References

    1. 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
    1. Rosenberger W, Haines L. Competing designs for phase I clinical trials: a review. Stat Med. 2002;21:2757‐2770. - PubMed
    1. 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
    1. 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.
    1. Temple R. Enrichment designs: Efficiency in development of cancer treatments. J Clin Oncol. 2005;23(22):4838‐4839. - PubMed

Publication types