Quantifying absolute treatment effect heterogeneity for time-to-event outcomes across different risk strata: divergence of conclusions with risk difference and restricted mean survival difference.

Background: Risk-based analyses are increasingly popular for understanding heterogeneous treatment effects (HTE) in clinical trials. For time-to-event analyses, the assumption that high-risk patients benefit most on the clinically important absolute scale when hazard ratios (HRs) are constant across risk strata might not hold. Absolute treatment effects can be measured as either the risk difference (RD) at a given time point or the difference in restricted mean survival time (ΔRMST) which aligns more closely with utilitarian medical decision-making frameworks.

Derivation and Validation of the PRECISE-HBR Score to Predict Bleeding After Percutaneous Coronary Intervention.

Background: Accurate bleeding risk stratification after percutaneous coronary intervention (PCI) is important for treatment individualization. However, there is still an unmet need for a more precise and standardized identification of high bleeding risk patients. We derived and validated a novel bleeding risk score by augmenting the PRECISE-DAPT score with the Academic Research Consortium for High Bleeding Risk (ARC-HBR) criteria.

Ten-year survival benefit and appropriateness of surgical versus percutaneous revascularization in synergy between percutaneous coronary intervention with Taxus and cardiac surgery randomized trial.

Objectives: Average treatment effects from randomized trials do not reflect the heterogeneity of an individual's response to treatment. This study evaluates the appropriate proportions of patients for coronary artery bypass grafting, or percutaneous intervention based on the predicted/observed ratio of 10-year all-cause mortality in the SYNTAX population.

Methods: The study included 1800 randomized patients and 1275 patients in the nested percutaneous (n = 198) or surgical (n = 1077) registries.

Guidance for unbiased predictive information for healthcare decision-making and equity (GUIDE): considerations when race may be a prognostic factor.

Clinical prediction models (CPMs) are tools that compute the risk of an outcome given a set of patient characteristics and are routinely used to inform patients, guide treatment decision-making, and resource allocation. Although much hope has been placed on CPMs to mitigate human biases, CPMs may potentially contribute to racial disparities in decision-making and resource allocation. While some policymakers, professional organizations, and scholars have called for eliminating race as a variable from CPMs, others raise concerns that excluding race may exacerbate healthcare disparities and this controversy remains unresolved.