ArticleStatistical methods in medical research2018
Comparison of statistical approaches dealing with time-dependent confounding in drug effectiveness studies.
Article in Statistical methods in medical research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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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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.
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Who cites it
10 citing papers in PubMed, 22 citations in OpenAlex.
- Bariatric metabolic surgery and cancer risk: Target trial emulation using iterative time distribution matching.Biomolecules & biomedicine · 2025Article
- Adjusting for selection bias due to missing eligibility criteria in emulated target trials.American journal of epidemiology · 2025Article
- Statistical Methods to Adjust for Treatment Switching in Real-World Clinical Studies: A Scoping Review and Descriptive Comparison.Clinical pharmacology and therapeutics · 2025Article
- Combining genetic proxies of drug targets and time-to-event analyses from longitudinal observational data to identify target patient populations.BMC cardiovascular disorders · 2025 · on this mapObservational
- Genetic matching for time-dependent treatments: a longitudinal extension and simulation study.BMC medical research methodology · 2023Article
- Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models.Statistics in medicine · 2023Observational
- Simulating longitudinal data from marginal structural models using the additive hazard model.Biometrical journal. Biometrische Zeitschrift · 2021Article
- Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data.International journal of epidemiology · 2020Article
- Performance of the marginal structural cox model for estimating individual and joined effects of treatments given in combination.BMC medical research methodology · 2017Article
- Statistical approaches in ambispective cohort studies: Challenges and methodological insights.Perspectives in clinical researchArticle
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6 authors at 3 institutions in 1 country.
Funding
Abstract
In longitudinal studies, if the time-dependent covariates are affected by the past treatment, time-dependent confounding may be present. For a time-to-event response, marginal structural Cox models are frequently used to deal with such confounding. To avoid some of the problems of fitting marginal structural Cox model, the sequential Cox approach has been suggested as an alternative. Although the estimation mechanisms are different, both approaches claim to estimate the causal effect of treatment by appropriately adjusting for time-dependent confounding. We carry out simulation studies to assess the suitability of the sequential Cox approach for analyzing time-to-event data in the presence of a time-dependent covariate that may or may not be a time-dependent confounder. Results from these simulations revealed that the sequential Cox approach is not as effective as marginal structural Cox model in addressing the time-dependent confounding. The sequential Cox approach was also found to be inadequate in the presence of a time-dependent covariate. We propose a modified version of the sequential Cox approach that correctly estimates the treatment effect in both of the above scenarios. All approaches are applied to investigate the impact of beta-interferon treatment in delaying disability progression in the British Columbia Multiple Sclerosis cohort (1995-2008).
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