Doubly Robust Estimation of Desirability of Outcome Ranking (DOOR) Probability

Thomas M. Menino Convention & Exhibition Center 

Covariate adjustment is an important tool in medical research for observational studies, and even clinical trial data, since the addition of regression models could improve precision by incorporating imbalanced covariates, and thus help make correct inference. Desirability of outcome ranking (DOOR) is a patient-centric benefit-risk evaluation methodology designed for randomized clinical trials. Still, robust covariate adjustment methods could further expand the compatibility of this method. In traditional DOOR analysis, each participant's outcome is ranked based on pre-specified clinical criteria, where the most desirable rank represents a good outcome with no side effects and the least desirable rank is the worst possible clinical outcome. We develop a causal framework for estimating the population-level DOOR probability, via inverse probability of treatment weighting method, G-Computation method and a Doubly Robust method that combines both. The performance of the proposed methodologies is examined through simulations. We also perform a causal analysis to Multi-Drug Resistant Organism (MDRO) network within Antibacterial Resistant Leadership Group (ARLG).

Causal Inference

G-computation

Infectious Disease

Multi-Drug Resistance

Inverse Probability Weighting

Observational Study 

Section on Statistics in Epidemiology