ArticleStatistica Sinica2018
SENSITIVITY ANALYSIS FOR UNMEASURED CONFOUNDING IN COARSE STRUCTURAL NESTED MEAN MODELS.
Article in Statistica Sinica, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Beyond "No Unmeasured Confounding": Challenges and Opportunities for Cornfield-Style Reasoning in Modern Causal Inference.Observational studies · 2026Article
- Data fusion methods for the heterogeneity of treatment effect and confounding function.Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability · 2025Article
- Article
- Estimating spatially varying health effects of wildland fire smoke using mobile health data.Journal of the Royal Statistical Society. Series C, Applied statistics · 2024Article
- Article
- Article
- Multiply robust matching estimators of average and quantile treatment effects.Scandinavian journal of statistics, theory and applications · 2023Article
- Semiparametric estimation of structural nested mean models with irregularly spaced longitudinal observations.Biometrics · 2022Article
- Utilizing stratified generalized propensity score matching to approximate blocked randomized designs with multiple treatment levels.Journal of biopharmaceutical statistics · 2022Article
- Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population.Journal of causal inference · 2022Article
- Robust inference of conditional average treatment effects using dimension reduction.Statistica Sinica · 2022Article
- Commentary: Using potential outcomes causal methods to assess whether reductions in PMGlobal epidemiology · 2021Article
- Semiparametric estimation of structural failure time models in continuous-time processes.Biometrika · 2020Article
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2 authors.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.