A Simple Sensitivity Analysis Method for Unmeasured Confounders via Linear Programming With Estimating Equation Constraints

Tang, Chengyao; Zhou, Yi; Huang, Ao et al.

Statistics in Medicine, 2025, 44(3-4), e10288

Number of Access:243（2025-08-15 18:59 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/100970

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Title
A Simple Sensitivity Analysis Method for Unmeasured Confounders via Linear Programming With Estimating Equation Constraints
Creator
CreatorName
Tang, Chengyao
CreatorName
Zhou, Yi
CreatorName
Huang, Ao
NRID
1000050425154 1000050425154
researchmap permalink
hattori_satoshi hattori_satoshi
Scopus Author ID
35728877500 35728877500
CreatorName
Hattori, Satoshi
Subject
average treatment effect
linear programming
sensitivity analysis
unmeasured confounders
Description
Tang C., Zhou Y., Huang A., et al. A Simple Sensitivity Analysis Method for Unmeasured Confounders via Linear Programming With Estimating Equation Constraints. Statistics in Medicine 44, e10288 (2025); https://doi.org/10.1002/sim.10288.
Abstract
In estimating the average treatment effect in observational studies, the influence of confounders should be appropriately addressed. To this end, the propensity score is widely used. If the propensity scores are known for all the subjects, bias due to confounders can be adjusted by using the inverse probability weighting (IPW) by the propensity score. Since the propensity score is unknown in general, it is usually estimated by the parametric logistic regression model with unknown parameters estimated by solving the score equation under the strongly ignorable treatment assignment (SITA) assumption. Violation of the SITA assumption and/or misspecification of the propensity score model can cause serious bias in estimating the average treatment effect (ATE). To relax the SITA assumption, the IPW estimator based on the outcome-dependent propensity score has been successfully introduced. However, it still depends on the correctly specified parametric model and its identification. In this paper, we propose a simple sensitivity analysis method for unmeasured confounders. In the standard practice, the estimating equation is used to estimate the unknown parameters in the parametric propensity score model. Our idea is to make inferences on the (ATE) by removing restrictive parametric model assumptions while still utilizing the estimating equation. Using estimating equations as constraints, which the true propensity scores asymptotically satisfy, we construct the worst-case bounds for the ATE with linear programming. Differently from the existing sensitivity analysis methods, we construct the worst-case bounds with minimal assumptions. We illustrate our proposal by simulation studies and a real-world example.
Publisher
John Wiley and Sons Ltd
Source Title
Statistics in Medicine
Volume (Issue)
44(3-4)
Page
e10288
Date of Issued
2025-01-24
Language
English
Handle URL
https://hdl.handle.net/11094/100970
PISSN
02776715
eISSN
10970258
Relation.isIdenticalTo
DOI (Publisher Version)
https://doi.org/10.1002/sim.10288
Access Rights
open access
CopyRight
This article is licensed under a Creative Commons Attribution 4.0 International License.
oaire:version
Version of Record
Category
学術雑誌論文 Journal Article