ArticleScandinavian journal of statistics, theory and applications2022
MULTIPLY ROBUST ESTIMATORS OF CAUSAL EFFECTS FOR SURVIVAL OUTCOMES.
Article in Scandinavian journal of statistics, theory and applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Long-Term Association of Cardiovascular Risk Burden With Dementia Risk: The Role of Life-Course Confounders in an Analysis of Cumulative and Specific Factors.Journal of the American Heart Association · 2026Article
- What Are We Estimating? Revisiting Standard Nutritional Models Through the Target Trial Framework.American journal of epidemiology · 2026Article
- A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding.Lifetime data analysis · 2025Article
- Empirical Sandwich Variance Estimator for Iterated Conditional Expectation g-Computation.Statistics in medicine · 2024Article
- A Review of Causal Inference Methods for Estimating the Effects of Exposure Change When Incident Exposure Is Unobservable.Current epidemiology reports · 2024Article
- Grace periods in comparative effectiveness studies of sustained treatments.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2024Article
- Article
- Intervention treatment distributions that depend on the observed treatment process and model double robustness in causal survival analysis.Statistical methods in medical research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Multiply robust estimators of the longitudinal g-formula have recently been proposed to protect against model misspecification better than the standard augmented inverse probability weighted estimator (Rotnitzky et al., 2017; Luedtke et al., 2018). These multiply robust estimators ensure consistency if one of the models for the treatment process or outcome process is correctly specified at each time point. We study the multiply robust estimators of Rotnitzky et al. (2017) in the context of a survival outcome. Specifically, we compare various estimators of the g-formula for survival outcomes in order to 1) understand how the estimators may be related to one another, 2) understand each estimator's robustness to model misspecification, and 3) construct estimators that can be more efficient than others in certain model misspecification scenarios. We propose a modification of the multiply robust estimators to gain efficiency under misspecification of the outcome model by using calibrated propensity scores over non-calibrated propensity scores at each time point. Theoretical results are confirmed via simulation studies, and a practical comparison of these estimators is conducted through an application to the US Veterans Aging Cohort Study.
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Registered trials
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.