A Novel Algorithm for Analysis of Multiple Endpoints Using Risk-Benefit Profiles

• Activity Number: 87 - Survival and Longitudinal/Clustered Data Analysis
• Type: Contributed
• Date/Time: Monday, August 9, 2021 : 10:00 AM to 11:50 AM
• Sponsor: Biometrics Section
• Author(s): Natalia A Gouskova* and Dae Hyun Kim and Sandra Shi and Thomas Travison
• Companies: Hebrew SeniorLife and Hebrew SeniorLife, Harvard Medical School, Beth Israel Deaconess Medical Center and Hebrew SeniorLife, Harvard Medical School, Beth Israel Deaconess Medical Center and Hebrew SeniorLife, Harvard Medical School, Beth Israel Deaconess Medical Center
• Keywords: risk-benefit profiles; competing risks; inverse probability of censoring weights; algorithm; implementation

Abstract

Often it is necessary to evaluate effectiveness of an intervention on the basis of multiple beneficial and harmful events. An attractive approach is to order combinations of these events based on desirability of the overall outcome (e.g. from cure without any adverse events to death), and then determine whether the intervention shifts the distribution of these ordered outcomes towards more desirable. In 2015 Claggett et al proposed a comprehensive method which implements this approach allowing nonparametric and regression-based inference in presence of competing risks. Key to the method is weighting observations by inverse probability of censoring (IPC) weights specific to subjects and event types. The method has seemingly great practical utility, but computation of weights is a non-trivial challenge with real-life data when each event can have its own censoring time. We present a novel recursive algorithm solving this problem for an arbitrary number of events ordered by clinical importance or desirability. The algorithm can be implemented in SAS or R software, and computes IPC weights, as well as nonparametric or parametric estimates and resampling-based measures of uncertainty.