Lancaster EPrints

Dose-escalation strategies which utilise subgroup information

Cotterill, Amy and Jaki, Thomas Friedrich (2018) Dose-escalation strategies which utilise subgroup information. Pharmaceutical Statistics, 17 (5). pp. 414-436. ISSN 1539-1604

PDF (BiomarkerPaper_30JAN18_v2_TJ) - Submitted Version
Available under License Creative Commons Attribution.

Download (686Kb) | Preview


    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.

    Item Type: Journal Article
    Journal or Publication Title: Pharmaceutical Statistics
    Additional Information: This is the peer reviewed version of the following article: Cotterill A, Jaki T. Dose‐escalation strategies which use subgroup information. Pharmaceutical Statistics. 2018;17:414–436. which has been published in final form at This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
    Departments: Faculty of Science and Technology > Mathematics and Statistics
    ID Code: 125763
    Deposited By: ep_importer_pure
    Deposited On: 08 Jun 2018 10:28
    Refereed?: Yes
    Published?: Published
    Last Modified: 20 Mar 2019 01:10
    Identification Number:

    Actions (login required)

    View Item