Doubly robust estimation and sensitivity analysis for marginal structural quantile models.
Biometrics
; 80(2)2024 Mar 27.
Article
in En
| MEDLINE
| ID: mdl-38884127
ABSTRACT
The marginal structure quantile model (MSQM) provides a unique lens to understand the causal effect of a time-varying treatment on the full distribution of potential outcomes. Under the semiparametric framework, we derive the efficiency influence function for the MSQM, from which a new doubly robust estimator is proposed for point estimation and inference. We show that the doubly robust estimator is consistent if either of the models associated with treatment assignment or the potential outcome distributions is correctly specified, and is semiparametric efficient if both models are correct. To implement the doubly robust MSQM estimator, we propose to solve a smoothed estimating equation to facilitate efficient computation of the point and variance estimates. In addition, we develop a confounding function approach to investigate the sensitivity of several MSQM estimators when the sequential ignorability assumption is violated. Extensive simulations are conducted to examine the finite-sample performance characteristics of the proposed methods. We apply the proposed methods to the Yale New Haven Health System Electronic Health Record data to study the effect of antihypertensive medications to patients with severe hypertension and assess the robustness of the findings to unmeasured baseline and time-varying confounding.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Computer Simulation
/
Models, Statistical
/
Hypertension
Limits:
Humans
Language:
En
Journal:
Biometrics
Year:
2024
Document type:
Article
Affiliation country:
Estados Unidos
Country of publication:
Reino Unido