Addressing heterogeneity in the design of phase II clinical trials in geriatric oncology

INTRODUCTION

Cancer in the elderly is a major public issue. However, older patients have long been debarred from clinical trials. There is a high unmet medical need for specific trials addressing oncology strategies adapted to older patients' conditions. While randomised phase III trials remain the gold standard, they usually require large numbers of patients. In this perspective, late single-arm phase II trials assessing treatment feasibility might prove a good alternative. However, it is essential to take into account the heterogeneity in an ageing population characterised by frailty. Standard parallel phase II studies in defined frail and non-frail populations also require a high number of patients. Used in molecular subtyping and treatment effect heterogeneity, stratified adaptive designs can improve statistical performance, but they have never been used in geriatric oncology. This report describes their potential benefits and useful applications as compared with standard designs.

METHODS

In a heterogeneous population, stratified adaptive designs allowed us to select subgroups of interest in two stages. Operational characteristics were evaluated through simulations of clinical trials under different scenarios.

RESULTS

Simulations showed that the use of stratified adaptive designs can efficiently minimise both the number of patients to be included and accrual duration with competitive statistical power and high heterogeneity detection rate at interim analysis.

CONCLUSION

Compared with classical phase II designs, stratified adaptive phase II trial methodology offers a promising approach to improve clinical research in geriatric oncology. These designs may also be efficient in other populations such as children or adolescents and young adults.

Designing phase II clinical trials to target subgroup of interest in a heterogeneous population: A case study using an R package

Phase II trials that evaluate target therapies based on a biomarker must be well designed in order to assess anti-tumor activity as well as clinical utility of the biomarker. Classical phase II designs do not deal with this molecular heterogeneity and can lead to an erroneous conclusion in the whole population, whereas a subgroup of patients may well benefit from the new therapy. Moreover, the target population to be evaluated in a phase III trial may be incorrectly specified. Alternative approaches are proposed in the literature that make it possible to include two subgroups according to biomarker status (negative/positive) in the same study. Jones, Parashar and Tournoux et al. propose different stratified adaptive two-stage designs to identify a subgroup of interest in a heterogeneous population that could possibly benefit from the experimental treatment at the end of the first or second stage. Nevertheless, these designs are rarely used in oncology research. After introducing these stratified adaptive designs, we present an R package (ph2hetero) implementing these methods. A case study is provided to illustrate both the designs and the use of the R package. These stratified adaptive designs provide a useful alternative to classical two-stage designs and may also provide options in contexts other than biomarker studies.