Select Contemporary Statistical Concepts in Heart Failure Clinical Trials: Insights From the Heart Failure Collaboratory.
Evolving statistical concepts and innovative trial designs for heart failure (HF) clinical trials seek to improve the conduct, efficiency, and likelihood of meaningful evidence generation crucial for advancing therapeutic development and optimizing patient care. HF trials with conventional statistical frameworks often require large sample sizes, long follow-up times, and high cost to generate sufficient evidence. Novel statistical methodologies would be of interest if they could address these issues while retaining or enhancing the clinical relevance and reliability of results. The HFC (Heart Failure Collaboratory), comprising clinical investigators, clinicians, statisticians, patients, government representatives, payors, and industry collaborators, leads efforts to improve HF research methodologies. HFC discussions have included statistical concepts such as the estimand framework, HR drift, and analytic methods, including the win ratio and restricted mean survival time, that have not been used frequently in HF trials. The estimand framework encourages precise definition and alignment of trial objectives with trial design. The win ratio method attempts to incorporate and prioritize multiple clinically meaningful outcomes by using a hierarchy of clinical importance. The restricted mean survival time provides an alternative to the HR as a measure of therapeutic effect by quantifying the mean time gained or lost during a fixed time after randomization. This paper provides a critical review of some evolving HF trial design methodologies and statistical concepts for the HF community as discussed within the HFC. Our goal is to foster collaboration among diverse stakeholders and advance the development of effective treatments and improve patient care outcomes.