BOIN‐ET: Bayesian optimal interval design for dose finding based on both efficacy and toxicity outcomes. (26th April 2018)

• Record Type: Journal Article
• Authors: Takeda, Kentaro; Taguri, Masataka; Morita, Satoshi
• Other Names: Morgan David guestEditor.
• Is Part Of: Pharmaceutical statistics. Volume 17:Number 4(2018)
• Page Start: 383
• Page End: 395
• Publication Date: 2018-04-26
• Subjects: Bayesian adaptive design -- dose finding -- efficacy‐toxicity -- interval design -- model‐assisted design -- optimal dose; Pharmacy -- Statistical methods -- Periodicals; Pharmacy -- Statistics -- Periodicals; 615.10727
• DOI: 10.1002/pst.1864
• Languages: English
• ISSNs: 1539-1604

Abstract

Abstract : One of the main purposes of a phase I dose‐finding trial in oncology is to identify an optimal dose (OD) that is both tolerable and has an indication of therapeutic benefit for subjects in subsequent phase II and III trials. Many phase I dose‐finding methods based solely on toxicity considerations have been proposed under the assumption that both toxicity and efficacy monotonically increase with the dose level. Such an assumption may not be necessarily the case, however, when evaluating the OD for molecular targeted, cytostatic, and biological agents, as well as immune‐oncology therapy. To address this issue, we extend the Bayesian optimal interval (BOIN) design, which is nonparametric and thus does not require the assumption used in model‐based designs, in order to identify an OD based on both efficacy and toxicity outcomes. The new design is named "BOIN‐ET." A simulation study is presented that includes a comparison of this proposed method to the model‐based approaches in terms of both efficacy and toxicity responses. The simulation shows that BOIN‐ET has advantages in both the percentages of correct ODs selected and the average number of patients allocated to the ODs across a variety of realistic settings.