Simulating longitudinal data from marginal structural models using the additive hazard model
| العنوان: | Simulating longitudinal data from marginal structural models using the additive hazard model |
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| المؤلفون: | Jon Michael Gran, Shaun R. Seaman, Ruth H. Keogh, Stijn Vansteelandt |
| المساهمون: | Keogh, Ruth H. [0000-0001-6504-3253], Seaman, Shaun R. [0000-0003-3726-5937], Vansteelandt, Stijn [0000-0002-4207-8733], Apollo - University of Cambridge Repository, Keogh, Ruth H [0000-0001-6504-3253], Seaman, Shaun R [0000-0003-3726-5937] |
| المصدر: | Biometrical journal. Biometrische Zeitschrift BIOMETRICAL JOURNAL |
| بيانات النشر: | arXiv, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | marginal structural model, FOS: Computer and information sciences, longitudinal data, Computer science, Marginal structural model, additive hazard model, 01 natural sciences, time-dependent confounding, survival analysis, 010104 statistics & probability, 0302 clinical medicine, Software, RESEARCH PAPERS, Econometrics, 030212 general & internal medicine, causal inference, Statistics, General Medicine, Outcome (probability), Mathematics and Statistics, SURVIVAL, INVERSE PROBABILITY WEIGHTS, Statistics, Probability and Uncertainty, RESEARCH PAPER, Statistics and Probability, Hazard (logic), Article, Methodology (stat.ME), 03 medical and health sciences, Covariate, Computer Simulation, 0101 mathematics, Statistics - Methodology, Proportional Hazards Models, Models, Statistical, Proportional hazards model, business.industry, COX, Probability and statistics, congenial models, simulation study, FAILURE TIME MODELS, Models, Structural, time‐dependent confounding, CAUSAL INFERENCE, Causal inference, Probability and Uncertainty, dependent confounding, time‐, business |
| الوصف: | Observational longitudinal data on treatments and covariates are increasingly used to investigate treatment effects, but are often subject to time‐dependent confounding. Marginal structural models (MSMs), estimated using inverse probability of treatment weighting or the g‐formula, are popular for handling this problem. With increasing development of advanced causal inference methods, it is important to be able to assess their performance in different scenarios to guide their application. Simulation studies are a key tool for this, but their use to evaluate causal inference methods has been limited. This paper focuses on the use of simulations for evaluations involving MSMs in studies with a time‐to‐event outcome. In a simulation, it is important to be able to generate the data in such a way that the correct forms of any models to be fitted to those data are known. However, this is not straightforward in the longitudinal setting because it is natural for data to be generated in a sequential conditional manner, whereas MSMs involve fitting marginal rather than conditional hazard models. We provide general results that enable the form of the correctly specified MSM to be derived based on a conditional data generating procedure, and show how the results can be applied when the conditional hazard model is an Aalen additive hazard or Cox model. Using conditional additive hazard models is advantageous because they imply additive MSMs that can be fitted using standard software. We describe and illustrate a simulation algorithm. Our results will help researchers to effectively evaluate causal inference methods via simulation. |
| وصف الملف: | application/pdf; text/xml |
| تدمد: | 0323-3847 1521-4036 |
| DOI: | 10.48550/arxiv.2002.03678 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::43c1d7581fcfb869c72227279544f328 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....43c1d7581fcfb869c72227279544f328 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 03233847 15214036 |
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| DOI: | 10.48550/arxiv.2002.03678 |