Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy in oncology

Abstract

The ability to select a sensitive patient population may be crucial for the development of a targeted therapy. Identifying such a population with an acceptable level of confidence may lead to an inflation in development time and cost. We present an approach that allows to decrease these costs and to increase the reliability of the population selection. It is based on an actual adaptive phase II/III design and uses Bayesian decision tools to select the population of interest at an interim analysis. The primary endpoint is assumed to be the time to some event like e.g. progression. It is shown that the use of appropriately stratified logrank tests in the adaptive test procedure guarantees overall type I error control also when using information on patients that are censored at the adaptive interim analysis. The use of Bayesian decision tools for the population selection decision making is discussed. Simulations are presented to illustrate the operating characteristics of the study design relative to a more traditional development approach. Estimation of treatment effects is considered as well.

Adaptivity in drug discovery and development

“…In an adaptive design, steps (2) and (3) can be combined into one clinical study, delivering more power to detect treatment benefits as compared with the more traditional approach. Brannath et al [2009] proposed an efficient three-stage design (with two interim analyses) to compare a new therapy with a control therapy for the treatment of a specific type of cancer. At the first interim analysis the treatment benefit is assessed in the overall population and separately for both the biomarker positive and negative patients, noting that the biomarker classifier is available from step (1) above.…”

“…However, often there is insufficient confidence at the outset to restrict the patient population. By comparison, adaptive designs, which allow the final assessment to focus on the biomarker positive population based on the cumulative information available at an interim analysis Wang et al, 2007;Brannath et al, 2009], can be employed.…”

Cancer Drug Development and the Evolving Regulatory Framework for Companion Diagnostics in the European Union

“…Tailored trial designs for this purpose have been proposed (8,(13)(14)(15)(16), as, for example, enrichment designs (where only biomarkerpositive patients are included), stratified designs (where the randomization is stratified by biomarker status), or adaptive enrichment designs (where first-stage biomarker-positive and biomarker-negative patients are included and there is an option to restrict randomization after an interim analysis to biomarker-positive patients only; refs. [17][18][19]. Although enrichment designs are most efficient to confirm a positive benefit-risk balance in the biomarker-positive population, they give no information on biomarker-negative patients.…”

Adaptive Methods: When and How Should They be Used in Clinical Trials?

“…Although a biomarker that can be related to treatment efficacy is available, adaptive designs, so-called enrichment designs, have been proposed. [35,36] They allow the trial to be started in the general population with the option of restricting inclusions to a sub-group following interim analysis if the results indicate that the treatment is effective only in this sub-group. This type of design does not raise any particular problems if the marker defining the sub-groups is specified in advance.…”