A novel Bayesian adaptive design incorporating both primary and secondary endpoints for randomized IIB chemoprevention study of women at increased risk for breast cancer

Abstract

Background: Our randomized controlled clinical trial will explore the potential of bazedoxifene plus conjugated estrogen to modulate breast tissue-based risk biomarkers as a surrogate for breast cancer risk reduction. This paper investigates the statistical design features of the trial and the rationale for the final choice of its design. Group sequential designs are a popular design approach to allow a trial to stop early for success or futility, potentially saving time and money over a fixed trial design. While Bayesian adaptive designs enjoy the same properties as group sequential designs, they have the added benefit of using prior information as well as inferential interpretation conditional on the data. Whether a frequentist or Bayesian trial, most adaptive designs have interim analyses that allow for early stopping, typically utilizing only the primary endpoint. A drawback to this approach is that the study may not have enough data for adequate comparisons of a single, key secondary endpoint. This can happen, for example, if the secondary endpoint has a smaller effect than the primary endpoint.

Methods: In this paper, we investigate a trial design called two-endpoint adaptive, which stops early only if a criterion is met for primary and secondary endpoints. The approach focuses the final analysis on the primary endpoint but ensures adequate data for the secondary analysis. Our study has two arms with a primary (change in mammographic fibroglandular volume) and secondary endpoint (change in mammary tissue Ki-67).

Results: We present operating characteristics including power, trial duration, and type I error rate and discuss the value and risks of modeling Bayesian group sequential designs with primary and secondary endpoints, comparing against alternative designs. The results indicate that the two-endpoint adaptive design has better operating characteristics than competing designs if one is concerned about having adequate information for a key secondary endpoint.

Discussion: Our approach balances trial speed and the need for information on the single, key secondary endpoint.

Keywords: Bayesian adaptive design; Breast Cancer prevention; Early phase; Fibroglandular volume, Ki-67; Group sequential monitoring.

Conflict of interest statement

The authors declare that they have no competing interests.

© 2022. The Author(s).

Figures

Fig. 1

A schematic of the two-endpoint adaptive design. The “n = 120 accrual?” asks if we have accrued 120 participants

Fig. 2

Expected sample size when primary endpoint (FGV) is a large (− 30 vs 0) and b very large (− 45 vs 0) and the secondary endpoint (Ki-67) varies in its effect

Fig. 3

Expected posterior standard deviation when primary endpoint (FGV) is a large (− 30 vs 0) or b very large (− 45 vs 0) and the secondary endpoint (Ki-67) varies in its effect