ArticleEuropean journal of epidemiology2014
Comparing a marginal structural model with a Cox proportional hazard model to estimate the effect of time-dependent drug use in observational studies: statin use for primary prevention of cardiovascular disease as an example from the Rotterdam Study.
Article in European journal of epidemiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Association between miRNAs and DEFA4 in coronary artery disease.Molecular biology reports · 2026Article
- Adherence to the Dietary Approaches to Stop Hypertension score and cardiovascular disease risk in Korean adults: a prospective cohort study using marginal structural models.The American journal of clinical nutrition · 2026Article
- Statistical Methods to Adjust for Treatment Switching in Real-World Clinical Studies: A Scoping Review and Descriptive Comparison.Clinical pharmacology and therapeutics · 2025Article
- Instrumental variable estimation of the marginal structural Cox model for time-varying treatments.Biometrika · 2023Article
- Impact of early heparin therapy on outcomes in patients with solid malignancy associated sepsis: a marginal structural model causal analyse.Frontiers in pharmacology · 2023Article
- Effectiveness of early heparin therapy on outcomes in critically ill patients with sepsis-induced coagulopathy.Frontiers in pharmacology · 2023Article
- Effectiveness of sodium bicarbonate infusion on mortality for elderly septic patients with acute metabolic acidosis.Frontiers in pharmacology · 2022Article
- Adherence toFrontiers in nutrition · 2022Article
- Clinical effectiveness of sodium bicarbonate therapy on mortality for septic patients with acute moderate lactic acidosis.Frontiers in pharmacology · 2022Article
- Effectiveness of sodium bicarbonate infusion on mortality in septic patients with metabolic acidosis.Intensive care medicine · 2018Article
- Statin Treatment and Clinical Outcomes of Heart Failure Among Africans: An Inverse Probability Treatment Weighted Analysis.Journal of the American Heart Association · 2017Article
- The impact of adherence to inhaled drugs on 5-year survival in COPD patients: a time dependent approach.Pharmacoepidemiology and drug safety · 2016Article
- Caution: work in progress : While the methodological "revolution" deserves in-depth study, clinical researchers and senior epidemiologists should not be disenfranchised.European journal of epidemiology · 2016Article
- The Rotterdam Study: 2016 objectives and design update.European journal of epidemiology · 2015Article
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Authors and funding
9 authors.
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Abstract
When studying the causal effect of drug use in observational data, marginal structural modeling (MSM) can be used to adjust for time-dependent confounders that are affected by previous treatment. The objective of this study was to compare traditional Cox proportional hazard models (with and without time-dependent covariates) with MSM to study causal effects of time-dependent drug use. The example of primary prevention of cardiovascular disease (CVD) with statins was examined using up to 17.7 years of follow-up from 4,654 participants of the observational prospective population-based Rotterdam Study. In the MSM model, the weight was based on measurements of established cardiovascular risk factors and co-morbidity. In general, we could not demonstrate important differences in results from the Cox models and MSM. Results from analysis on duration of statin use suggested that substantial residual confounding by indication was not accounted for during the period shortly after statin initiation. In conclusion, although on theoretical grounds MSM is an elegant technique, lack of data on the precise time-dependent confounders, such as indication of treatment or other considerations of the prescribing physician jeopardizes the calculation of valid weights. Confounding remains a hurdle in observational effectiveness research on preventive drugs with a multitude of prescription determinants.
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