ArticleBiometrics2021
Parametric g-formula implementations for causal survival analyses.
Article in Biometrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed.
- Flexible Survival Extrapolation with Blended Hazards: Accounting for Treatment Effect Waning in Health Technology Assessment.Medical decision making : an international journal of the Society for Medical Decision Making · 2026Article
- Initiation of Gabapentin with Opioids and Risk of Respiratory Cardiovascular and Opioid-Related Adverse Events Following Surgery for Breast Cancer.Clinical pharmacology and therapeutics · 2026Article
- PCSK9 inhibitor treatment and outcomes in patients with atherosclerotic cardiovascular disease but without prior ischaemic events: an observational study.European heart journal · 2026Observational
- Improving Inference in Air Pollution Epidemiology: The Case for Rethinking Multipollutant Adjustment.Epidemiology (Cambridge, Mass.) · 2026Article
- 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
- Time-Varying Hormonal Treatment and Metastasis-Free Survival Among ER+ Breast Cancer Patients: A Natural History Modelling Approach.Statistics in medicine · 2026Article
- The effect of long-term adherence to physical activity recommendations in midlife on plasma proteins associated with frailty in the Atherosclerosis Risk in Communities (ARIC) study.European journal of epidemiology · 2026Article
- Estimating average causal effects with incomplete exposure and confounders.Journal of causal inference · 2026Article
- Identifying and avoiding design related biases in observational studies using the target trial framework.BMJ medicine · 2026Article
- Statistical methods for cost-effectiveness analysis of left-truncated and right-censored survival data with treatment delays.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- Dynamic associations of cholinesterase inhibitors and memantine with cognitive trajectories in individuals with Alzheimer's or mixed dementia: a real-world analysis using the quality registry SveDem.Alzheimer's research & therapy · 2025Observational
- Competing classes confront competing risks: unraveling mortality inequities with parametric g-computation.American journal of epidemiology · 2025Article
- G-formula with multiple imputation for causal inference with incomplete data.Statistical methods in medical research · 2025Article
- A Bayesian Approach to the G-Formula via Iterative Conditional Regression.Statistics in medicine · 2025Article
- A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding.Lifetime data analysis · 2025Article
- Guidelines and Best Practices for the Use of Targeted Maximum Likelihood and Machine Learning When Estimating Causal Effects of Exposures on Time-To-Event Outcomes.Statistics in medicine · 2025Article
- Article
- Empirical Sandwich Variance Estimator for Iterated Conditional Expectation g-Computation.Statistics in medicine · 2024Article
- Addressing treatment switching in the ALTA-1L trial with g-methods: exploring the impact of model specification.BMC medical research methodology · 2024Article
- A Review of Causal Inference Methods for Estimating the Effects of Exposure Change When Incident Exposure Is Unobservable.Current epidemiology reports · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The g-formula can be used to estimate the survival curve under a sustained treatment strategy. Two available estimators of the g-formula are noniterative conditional expectation and iterative conditional expectation. We propose a version of the iterative conditional expectation estimator and describe its procedures for deterministic and random treatment strategies. Also, because little is known about the comparative performance of noniterative and iterative conditional expectation estimators, we explore their relative efficiency via simulation studies. Our simulations show that, in the absence of model misspecification and unmeasured confounding, our proposed iterative conditional expectation estimator and the noniterative conditional expectation estimator are similarly efficient, and that both are at least as efficient as the classical iterative conditional expectation estimator. We describe an application of both noniterative and iterative conditional expectation to answer "when to start" treatment questions using data from the HIV-CAUSAL Collaboration.
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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.