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Doubly Robust Estimation of Desirability of Outcome Ranking (DOOR) Probability with Application to MDRO Studies

Shiyu Shu, Toshimitsu Hamasaki, Scott Evans, Lauren Komarow, David van Duin, Guoqing Diao

TL;DR

The paper addresses estimating the population DOOR probability $D$ in observational data by introducing three covariate-adjusted approaches: IPTW, G-Formula, and a Doubly Robust estimator that combines both. It establishes asymptotic properties via Influence Functions and the Delta Method, and provides practical implementation guidance using the lava R package. Through simulations and an MDRO study within ARLG, the authors show that covariate adjustment reduces bias and improves inference, with the Doubly Robust method offering protection against model misspecification. The work enables robust, patient-centered benefit–risk assessment in real-world observational studies and outlines directions for extending DOOR analysis to broader causal estimands and settings.

Abstract

In observational studies, adjusting for confounders is required if a treatment comparison is planned. A crude comparison of the primary endpoint without covariate adjustment will suffer from biases, and 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 observational studies. In 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 the 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 of the Multi-Drug Resistant Organism (MDRO) network within the Antibacterial Resistant Leadership Group (ARLG), comparing the benefit:risk between Mono-drug therapy and Combination-drug therapy.

Doubly Robust Estimation of Desirability of Outcome Ranking (DOOR) Probability with Application to MDRO Studies

TL;DR

The paper addresses estimating the population DOOR probability in observational data by introducing three covariate-adjusted approaches: IPTW, G-Formula, and a Doubly Robust estimator that combines both. It establishes asymptotic properties via Influence Functions and the Delta Method, and provides practical implementation guidance using the lava R package. Through simulations and an MDRO study within ARLG, the authors show that covariate adjustment reduces bias and improves inference, with the Doubly Robust method offering protection against model misspecification. The work enables robust, patient-centered benefit–risk assessment in real-world observational studies and outlines directions for extending DOOR analysis to broader causal estimands and settings.

Abstract

In observational studies, adjusting for confounders is required if a treatment comparison is planned. A crude comparison of the primary endpoint without covariate adjustment will suffer from biases, and 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 observational studies. In 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 the 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 of the Multi-Drug Resistant Organism (MDRO) network within the Antibacterial Resistant Leadership Group (ARLG), comparing the benefit:risk between Mono-drug therapy and Combination-drug therapy.
Paper Structure (10 sections, 1 theorem, 16 equations, 1 figure, 4 tables)

This paper contains 10 sections, 1 theorem, 16 equations, 1 figure, 4 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. IPTW/Propensity Score Method (PS)
  4. G-Formula/Potential Outcome Method (PO)
  5. Doubly Robust Method (DR)
  6. Asymptotic Results
  7. Results
  8. Simulation
  9. Real-world application
  10. Discussion

Key Result

Theorem 1

Let $O_i = (Y_i, Z_i, \mathbf{X}_i),i=1,\cdots,n$ be i.i.d. observations from a distribution $P_0$, and let $\psi(P)=[p_{11},p_{12},\cdots,p_{1(k-1)},p_{01},\cdots,p_{0(k-1)}]$ be a real-valued causal parameter vector with true value $\psi_0 = \psi(P_0)$. Assumption A1 (Identification). The paramete where $\Phi$ is the vector of influence functions of $\psi$ under $P_0$. Assumption A3 (Moment Cond

Figures (1)

  • Figure 1: Forest plot of Sequentially Dichotomized DOOR Probability Estimate with the Doubly Robust method

Theorems & Definitions (1)

  • Theorem 1: Asymptotic Normality of Influence-Function Estimators