ABSTRACT
The diversity of patient journeys can raise fundamental questions regarding the evaluation of drug effects in clinical trials to inform clinical practice. When defining the treatment effect of interest in a trial, the researcher needs to account for events occurring after treatment initiation, such as the start of a new therapy, before observing the end point. We review the newly introduced estimand framework to structure discussions on the relationship between patient journeys and the treatment effect of interest in oncology trials. In 2017, the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use released a draft addendum to its E9 guideline. The addendum introduces the concept of an estimand to precisely describe the treatment effect of interest. This estimand framework provides a structured approach to discuss how to account for intercurrent events that occur after random assignment and may affect the assessment or interpretation of the treatment effect. The framework is expected to improve coherence between trial objectives, design, analysis, and interpretation, as illustrated by examples in oncology disease settings. The estimand framework was applied to design a trial for a chimeric antigen receptor T-cell therapy. The treatment effect of interest was carefully defined considering the range of patient journeys expected for this particular indication and treatment. The trial design was developed accordingly to assess that treatment effect. All parties involved in the design of clinical trials need to consider possible patient journeys to define appropriate treatment effects and corresponding trial designs and analysis strategies. The estimand framework provides a common language to address the complexity introduced by varied patient journeys.
ABSTRACT
Understanding the dose-response relationship is a key objective in Phase II clinical development. Yet, designing a dose-ranging trial is a challenging task, as it requires identifying the therapeutic window and the shape of the dose-response curve for a new drug on the basis of a limited number of doses. Adaptive designs have been proposed as a solution to improve both quality and efficiency of Phase II trials as they give the possibility to select the dose to be tested as the trial goes. In this article, we present a 'shapebased' two-stage adaptive trial design where the doses to be tested in the second stage are determined based on the correlation observed between efficacy of the doses tested in the first stage and a set of pre-specified candidate dose-response profiles. At the end of the trial, the data are analyzed using the generalized MCP-Mod approach in order to account for model uncertainty. A simulation study shows that this approach gives more precise estimates of a desired target dose (e.g. ED70) than a single-stage (fixed-dose) design and performs as well as a two-stage D-optimal design. We present the results of an adaptive model-based dose-ranging trial in multiple sclerosis that motivated this research and was conducted using the presented methodology.
