Observational studyStatistics in medicine2023
Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models.
Observational study in Statistics in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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.
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
27 citing papers in PubMed.
- Effectiveness of the 2025-2026 seasonal influenza vaccine among U.S. veterans: an observational study.EClinicalMedicine · 2026Article
- Causal Inference Approaches to Estimating the Effect of Ibrutinib Dose Modifications on Progression-Free Survival in Chronic Lymphocytic Leukemia.Clinical pharmacology and therapeutics · 2026Article
- Causal graph neural networks for healthcare.Nature biomedical engineering · 2026Review
- Risk of Major Adverse Cardiovascular Events With Dolutegravir Versus Efavirenz-Based Antiretroviral Therapy: Emulated Target Trials Using Routine, De-identified Data From South Africa.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026Article
- Incidence Patterns and Prognostic Impact of Lung-Involving Multiple Primary Cancers: A 10-Year Hospital-Based Cohort Study.Thoracic cancer · 2026Article
- Risk of obesity, diabetes, hypertension, and major adverse cardiovascular events after a switch to an integrase inhibitor: a target trial emulation in REPRIEVE.The lancet. HIV · 2026Article
- Estimating conditional survival benefit for the allocation of scarce resources.Statistical methods in medical research · 2026Article
- Statistical Methods in Aging Research: Improving Current Practices and Embracing Emerging Approaches.Annual review of statistics and its application · 2026Article
- Accumulation and attenuation of cardiovascular benefit with glucagon-like peptide 1 receptor agonists.BMJ medicine · 2026Article
- Association between transitioning to a dolutegravir-based regimen and risk of incident hypertension in adults with HIV in West Africa: a multicentre target trial emulation study.EClinicalMedicine · 2025Article
- Opportunities, challenges and future perspectives for target trial emulation in critical care clinical research.Critical care (London, England) · 2025Review
- Context Is Medicine: Integrating the Exposome into Neurorehabilitation.Brain sciences · 2025Article
- Article
- Inference procedures in sequential trial emulation with survival outcomes: Comparing confidence intervals based on the sandwich variance estimator, bootstrap and jackknife.Statistical methods in medical research · 2025Article
- Causal estimation of time-varying treatments in observational studies: a scoping review of methods, applications, and missing data practices.BMC medical research methodology · 2025Article
- Liberal or restrictive transfusion for veno-arterial extracorporeal membrane oxygenation patients: a target trial emulation using the OBLEX study data.Critical care (London, England) · 2025Observational
- Effect of initiation of invasive ventilation in acute hypoxemic respiratory failure using sequential trials: treatment efficacy instead of timing.Critical care (London, England) · 2025Article
- Methodologies for the Emulation of Biomarker-Guided Trials Using Observational Data: A Systematic Review.Journal of personalized medicine · 2025Review
- A Pseudo-Value Approach to Causal Deep Learning of Semi-Competing Risks.Arabian journal of mathematics · 2025Article
- 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
5 authors.
Funding
Abstract
Longitudinal observational data on patients can be used to investigate causal effects of time-varying treatments on time-to-event outcomes. Several methods have been developed for estimating such effects by controlling for the time-dependent confounding that typically occurs. The most commonly used is marginal structural models (MSM) estimated using inverse probability of treatment weights (IPTW) (MSM-IPTW). An alternative, the sequential trials approach, is increasingly popular, and involves creating a sequence of "trials" from new time origins and comparing treatment initiators and non-initiators. Individuals are censored when they deviate from their treatment assignment at the start of each "trial" (initiator or noninitiator), which is accounted for using inverse probability of censoring weights. The analysis uses data combined across trials. We show that the sequential trials approach can estimate the parameters of a particular MSM. The causal estimand that we focus on is the marginal risk difference between the sustained treatment strategies of "always treat" vs "never treat." We compare how the sequential trials approach and MSM-IPTW estimate this estimand, and discuss their assumptions and how data are used differently. The performance of the two approaches is compared in a simulation study. The sequential trials approach, which tends to involve less extreme weights than MSM-IPTW, results in greater efficiency for estimating the marginal risk difference at most follow-up times, but this can, in certain scenarios, be reversed at later time points and relies on modelling assumptions. We apply the methods to longitudinal observational data from the UK Cystic Fibrosis Registry to estimate the effect of dornase alfa on survival.
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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.