Model-Based Meta-Analysis for Multiple Myeloma: A Quantitative Drug-Independent Framework for Efficient Decisions in Oncology Drug Development

Abstract

The failure rate for phase III trials in oncology is high; quantitative predictive approaches are needed. We developed a model-based meta-analysis (MBMA) framework to predict progression-free survival (PFS) from overall response rates (ORR) in relapsed/refractory multiple myeloma (RRMM), using data from seven phase III trials. A Bayesian analysis was used to predict the probability of technical success (PTS) for achieving desired phase III PFS targets based on phase II ORR data. The model demonstrated a strongly correlated (R² = 0.84) linear relationship between ORR and median PFS. As a representative application of the framework, MBMA predicted that an ORR of ∼66% would be needed in a phase II study of 50 patients to achieve a target median PFS of 13.5 months in a phase III study. This model can be used to help estimate PTS to achieve gold-standard targets in a target product profile, thereby enabling objectively informed decision-making.

Figure 1

Relationships between (a) ORR and (b) ≥VGPR rate and median PFS, using data from seven phase III studies in patients with RRMM. The blue lines show the linear regression and the gray bands represent the 95% confidence intervals.

Figure 2

Illustrative example of predicting PFS using ORR in the MBMA model. Probability of achieving target median PFS of 15 months is 34% (purple area) and probability of achieving minimum detectable PFS is 60% (blue area) for an ORR of 60% estimated in a study of 50 RRMM patients.

Figure 3

Predicted probability of achieving the target median PFS and minimal detectable median PFS in a phase III study for various observed ORRs in a phase II study.

Figure 4

Bayesian predictive probability of achieving (a) minimal detectable median PFS and (b) target median PFS, based on different correlation strengths between ORR and median PFS.