Sensitivity Analysis for Unmeasured Confounding in Coarse Structural Nested Mean Models

2017/02/14 by Shu Yang, Judith J. Lok · 2 citations
Mathematics · #Advanced Causal Inference Techniques #Statistical Methods and Bayesian Inference #Statistical Methods and Inference

paper · doi:10.5705/ss.202016.0133

openalex publication_date 2017/02/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/24

Abstract

Coarse Structural Nested Mean Models (SNMMs, Robins (2000)) and G-estimation can be used to estimate the causal effect of a time-varying treatment from longitudinal observational studies. However, they rely on an untestable assumption of no unmeasured confounding. In the presence of unmeasured confounders, the unobserved potential outcomes are not missing at random, and standard G-estimation leads to biased effect estimates. To remedy this, we investigate the sensitivity of G-estimators of coarse SNMMs to unmeasured confounding, assuming a nonidentifiable bias function which quantifies the impact of unmeasured confounding on the average potential outcome. We present adjusted G-estimators of coarse SNMM parameters and prove their consistency, under the bias modeling for unmeasured confounding. We apply this to a sensitivity analysis for the effect of the ART initiation time on the mean CD4 count at year 2 after infection in HIV-positive patients, based on the prospective Acute and Early Disease Research Program.

Citations

• Inference and missing data
• Estimating causal effects of treatments in randomized and nonrandomized studies.
• Multiple-Bias Modelling for Analysis of Observational Data

Cited by

• Multivariate Zero-Inflated Causal Model for Regional Mobility Restriction Effects on Consumer Spending
• Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning

Related

• Semiparametric efficient estimation of structural nested mean models with irregularly spaced observations
• Sensitivity Analysis for Marginal Structural Models
• Optimal estimation of coarse structural nested mean models with application to initiating ART in HIV infected patients
• Inference about ATE from Observational Studies with Continuous Outcome and Unmeasured Confounding
• Sensitivity models and bounds under sequential unmeasured confounding in longitudinal studies