Understanding Marginal Structural Models for Time-Varying Exposures: Pitfalls and Tips
| العنوان: | Understanding Marginal Structural Models for Time-Varying Exposures: Pitfalls and Tips |
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| المؤلفون: | Tomohiro Shinozaki, Etsuji Suzuki |
| المصدر: | Journal of Epidemiology Journal of Epidemiology, Vol 30, Iss 9, Pp 377-389 (2020) |
| بيانات النشر: | Japan Epidemiological Association, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Counterfactual thinking, marginal structural model, time-varying exposure, Time Factors, Epidemiology, Computation, Marginal structural model, 030209 endocrinology & metabolism, Feature selection, 03 medical and health sciences, Special Article, 0302 clinical medicine, Econometrics, Medicine, Humans, 030212 general & internal medicine, causal inference, Probability, lcsh:R5-920, Models, Statistical, business.industry, Inverse probability weighting, Confounding, g-formula, General Medicine, Reference Standards, Theory and Statistics, Causality, Models, Structural, Specification, Causal inference, lcsh:Medicine (General), business, inverse probability weighting |
| الوصف: | Epidemiologists are increasingly encountering complex longitudinal data, in which exposures and their confounders vary during follow-up. When a prior exposure affects the confounders of the subsequent exposures, estimating the effects of the time-varying exposures requires special statistical techniques, possibly with structural (ie, counterfactual) models for targeted effects, even if all confounders are accurately measured. Among the methods used to estimate such effects, which can be cast as a marginal structural model in a straightforward way, one popular approach is inverse probability weighting. Despite the seemingly intuitive theory and easy-to-implement software, misunderstandings (or “pitfalls”) remain. For example, one may mistakenly equate marginal structural models with inverse probability weighting, failing to distinguish a marginal structural model encoding the causal parameters of interest from a nuisance model for exposure probability, and thereby failing to separate the problems of variable selection and model specification for these distinct models. Assuming the causal parameters of interest are identified given the study design and measurements, we provide a step-by-step illustration of generalized computation of standardization (called the g-formula) and inverse probability weighting, as well as the specification of marginal structural models, particularly for time-varying exposures. We use a novel hypothetical example, which allows us access to typically hidden potential outcomes. This illustration provides steppingstones (or “tips”) to understand more concretely the estimation of the effects of complex time-varying exposures. |
| اللغة: | English |
| تدمد: | 1349-9092 0917-5040 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::26b98730490cc86756aff2a5e1a421f9Test http://europepmc.org/articles/PMC7429147Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....26b98730490cc86756aff2a5e1a421f9 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 13499092 09175040 |
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