Trends in endpoint selection in clinical trials of advanced breast cancer

The Evolution of Clinical Trials in Metastatic Breast Cancer: Design Features and Endpoints That Matter

The evolution of thought in assessing benefit in clinical trials of systemic therapy for metastatic breast cancer (MBC) is well documented, with most agents garnering regulatory approval based either on an advantage in overall survival (OS), time to progression (TTP), or progression-free survival (PFS) over an existing standard of care or objective response rate (ORR). Previous guidance for industry on clinical trial endpoints for the approval of cancer drugs and biologics was provided by the U.S. Food and Drug Administration (FDA) in 2007 and recently updated in 2018. The more recent FDA guidance recognizes that advances in science are facilitating the development of oncology products, which "may also result in the identification of additional endpoints that may be used to support approval of oncology products." This article critically addressed the evolution of thought on the advancement of clinical trials in MBC, from various stakeholder perspectives. Despite the term "stakeholder," the objective of all co-authors and parties concerned is to promote and inform the optimal design, conduct, and reporting of clinical trials for women with advanced breast cancer toward improving and extending lives. This article provides an overview of the evolving perspectives on this issue from the physician, regulatory agency, and patient and/or advocate points of view.

The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration

The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed to promote the conduct of, and provide guidance for, predictive analyses of heterogeneity of treatment effects (HTE) in clinical trials. The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risk with versus without the intervention, taking into account all relevant patient attributes simultaneously, to support more personalized clinical decision making than can be made on the basis of only an overall average treatment effect. The authors distinguished 2 categories of predictive HTE approaches (a "risk-modeling" and an "effect-modeling" approach) and developed 4 sets of guidance statements: criteria to determine when risk-modeling approaches are likely to identify clinically meaningful HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. They discuss limitations of these methods and enumerate research priorities for advancing methods designed to generate more personalized evidence. This explanation and elaboration document describes the intent and rationale of each recommendation and discusses related analytic considerations, caveats, and reservations.