ArticleStatistics in medicine2012
A simulation study of finite-sample properties of marginal structural Cox proportional hazards models.
Article in Statistics in medicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Challenges in Obtaining Valid Causal Effect Estimates with Machine Learning Algorithms.American journal of epidemiology · 2023Article
- A Simulation Study Comparing the Performance of Time-Varying Inverse Probability Weighting and G-Computation in Survival Analysis.American journal of epidemiology · 2023Article
- Association of Vitamin K and Non-Vitamin K Oral Anticoagulant Use and Cancer Incidence in Atrial Fibrillation Patients.Clinical pharmacology and therapeutics · 2022Observational
- SIMULATION IN PRACTICE: THE BALANCING INTERCEPT.American journal of epidemiology · 2021Article
- Evaluation of Survival Outcomes of Endovascular Versus Open Aortic Repair for Abdominal Aortic Aneurysms with a Big Data Approach.Entropy (Basel, Switzerland) · 2020Article
- Weighing the Evidence of Efficacy of Oral PrEP for HIV Prevention in Women in Southern Africa.AIDS research and human retroviruses · 2018Review
- Estimating multiple time-fixed treatment effects using a semi-Bayes semiparametric marginal structural Cox proportional hazards regression model.Biometrical journal. Biometrische Zeitschrift · 2018Article
- Performance of the marginal structural cox model for estimating individual and joined effects of treatments given in combination.BMC medical research methodology · 2017Article
- Treatment decisions in multiple sclerosis - insights from real-world observational studies.Nature reviews. Neurology · 2017Review
- Propensity Score Weighting Compared to Matching in a Study of Dabigatran and Warfarin.Drug safety · 2017Article
- Inverse probability weighting to estimate causal effect of a singular phase in a multiphase randomized clinical trial for multiple myeloma.BMC medical research methodology · 2016Article
- Marginal structural Cox models for estimating the association between β-interferon exposure and disease progression in a multiple sclerosis cohort.American journal of epidemiology · 2014Observational
- Simulation from a known Cox MSM using standard parametric models for the g-formula.Statistics in medicine · 2014Article
- Comparison of methods for estimating the effect of salvage therapy in prostate cancer when treatment is given by indication.Statistics in medicine · 2014Article
- Causal inference in occupational epidemiology: accounting for the healthy worker effect by using structural nested models.American journal of epidemiology · 2013Article
- Assessing the effect of hormonal contraception on HIV acquisition in observational data: challenges and recommended analytic approaches.AIDS (London, England) · 2013Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Motivated by a previously published study of HIV treatment, we simulated data subject to time-varying confounding affected by prior treatment to examine some finite-sample properties of marginal structural Cox proportional hazards models. We compared (a) unadjusted, (b) regression-adjusted, (c) unstabilized, and (d) stabilized marginal structural (inverse probability-of-treatment [IPT] weighted) model estimators of effect in terms of bias, standard error, root mean squared error (MSE), and 95% confidence limit coverage over a range of research scenarios, including relatively small sample sizes and 10 study assessments. In the base-case scenario resembling the motivating example, where the true hazard ratio was 0.5, both IPT-weighted analyses were unbiased, whereas crude and adjusted analyses showed substantial bias towards and across the null. Stabilized IPT-weighted analyses remained unbiased across a range of scenarios, including relatively small sample size; however, the standard error was generally smaller in crude and adjusted models. In many cases, unstabilized weighted analysis showed a substantial increase in standard error compared with other approaches. Root MSE was smallest in the IPT-weighted analyses for the base-case scenario. In situations where time-varying confounding affected by prior treatment was absent, IPT-weighted analyses were less precise and therefore had greater root MSE compared with adjusted analyses. The 95% confidence limit coverage was close to nominal for all stabilized IPT-weighted but poor in crude, adjusted, and unstabilized IPT-weighted analysis. Under realistic scenarios, marginal structural Cox proportional hazards models performed according to expectations based on large-sample theory and provided accurate estimates of the hazard ratio.
Indexed as
Identifiers
What OpenQuestion holds
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.