A Unified Causal Inference Framework for the Desirability of Outcome Ranking Paradigm in Benefit-Risk Evaluation

2026-08-05

AI Summary

This study addresses the absence of a unified causal inference framework for estimating the probability of Desirability of Ordered Random Outcomes (DOOR) in benefit–risk assessment. The authors propose the first covariate-adjusted, unified causal inference framework that expresses DOOR probabilities as bilinear functionals of marginal ordered outcome distributions under two treatment strategies. Conditional outcome distributions are estimated via sequential risk-set hazard models, and the corresponding efficient influence function is derived. The framework integrates G-computation, normalized inverse probability weighting (IPW), augmented IPW (AIPW), and targeted maximum likelihood estimation (TMLE), employing generalized linear models or Super Learner to estimate nuisance functions and incorporating cross-fitting to enhance robustness. The proposed CVTMLE-SL estimator demonstrates superior performance in terms of DOOR estimation bias, recovery of ordered outcome distributions, standard error accuracy, and confidence interval coverage, with empirical validation on real-world antimicrobial resistance data.

Abstract

We developed a unified covariate-adjusted causal inference framework for estimating the desirability of outcome ranking (DOOR) probability for benefit-risk evaluation in randomized trials and observational studies. The framework expresses the DOOR probability as a bilinear functional of the marginal ordinal outcome distributions under the two treatment strategies, estimates conditional ordinal distributions through sequential risk-set hazards, and derives the efficient influence function (EIF) of the DOOR probability. The point-estimation simulations compared G-computation, normalized inverse probability weighting (IPW), augmented IPW (AIPW), and targeted maximum likelihood estimation (TMLE), with nuisance functions estimated using generalized linear models or Super Learner (SL). TMLE-SL showed the strongest and most consistent point-estimation performance, with AIPW-SL ranking second. EIF-based inference was then evaluated for AIPW-SL and TMLE-SL, with and without cross-fitting, across settings varying in overlap, treatment-effect heterogeneity, and treatment allocation. CVTMLE-SL showed the strongest overall performance across DOOR-scale bias, recovery of the underlying ordinal distributions, standard-error accuracy, and confidence-interval coverage. We illustrate the methodology using data from the multidrug-resistant organism network of the Antibacterial Resistance Leadership Group.

Problem

• benefit-risk evaluation
• desirability of outcome ranking
• causal inference
• ordinal outcomes
• DOOR probability

Innovation

• DOOR
• causal inference
• efficient influence function
• TMLE
• Super Learner