ArticleBiometrika2016
A goodness-of-fit test for structural nested mean models.
Article in Biometrika, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- 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
- Semiparametric estimation of structural nested mean models with irregularly spaced longitudinal observations.Biometrics · 2022Article
- Semiparametric estimation of structural failure time models in continuous-time processes.Biometrika · 2020Article
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Authors and funding
2 authors.
Funding
Abstract
Coarse structural nested mean models are tools to estimate treatment effects from longitudinal observational data with time-dependent confounding. There is, however, no guidance on how to specify the treatment effect model, and model misspecification can lead to bias. We derive a goodness-of-fit test based on modified overidentification restrictions tests for evaluating a treatment effect model, and show that our test statistic is doubly-robust in the sense that, with a correct treatment effect model, the test has the correct type-I error if either the treatment initiation model or a nuisance regression outcome model is correctly specified. In a simulation study we show that the test has correct type-I error and can detect model misspecification. We use the test to study how the timing of antiretroviral treatment initiation after HIV infection predicts the effect of one year of treatment in HIV-positive patients with acute and early infection.
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