Individualized prescriptive inference in ischaemic stroke
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866911212362530816 |
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| author | Giles, Dominic Foulon, Chris Pombo, Guilherme Ruffle, James K. Xu, Tianbo Jäger, H. Rolf Cardoso, Jorge Ourselin, Sebastien Rees, Geraint Jha, Ashwani Nachev, Parashkev |
| author_facet | Giles, Dominic Foulon, Chris Pombo, Guilherme Ruffle, James K. Xu, Tianbo Jäger, H. Rolf Cardoso, Jorge Ourselin, Sebastien Rees, Geraint Jha, Ashwani Nachev, Parashkev |
| contents | The gold standard in the treatment of ischaemic stroke is set by evidence from randomized controlled trials, based on simple descriptions of presumptively homogeneous populations. Yet the manifest complexity of the brain's functional, connective, and vascular architectures introduces heterogeneities that violate the underlying statistical premisses, potentially leading to substantial errors at both individual and population levels. The counterfactual nature of interventional inference renders quantifying the impact of this defect difficult. Here we conduct a comprehensive series of semi-synthetic, biologically plausible, virtual interventional trials across 100M+ distinct simulations. We generate empirically grounded virtual trial data from large-scale meta-analytic connective, functional, genetic expression, and receptor distribution data, with high-resolution maps of 4K+ acute ischaemic lesions. Within each trial, we estimate treatment effects using models varying in complexity, in the presence of increasingly confounded outcomes and noisy treatment responses. Individualized prescriptions inferred from simple models, fitted to unconfounded data, were less accurate than those from complex models, fitted to confounded data. Our results indicate that complex modelling with richly represented lesion data is critical to individualized prescriptive inference in ischaemic stroke. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_10748 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Individualized prescriptive inference in ischaemic stroke Giles, Dominic Foulon, Chris Pombo, Guilherme Ruffle, James K. Xu, Tianbo Jäger, H. Rolf Cardoso, Jorge Ourselin, Sebastien Rees, Geraint Jha, Ashwani Nachev, Parashkev Quantitative Methods The gold standard in the treatment of ischaemic stroke is set by evidence from randomized controlled trials, based on simple descriptions of presumptively homogeneous populations. Yet the manifest complexity of the brain's functional, connective, and vascular architectures introduces heterogeneities that violate the underlying statistical premisses, potentially leading to substantial errors at both individual and population levels. The counterfactual nature of interventional inference renders quantifying the impact of this defect difficult. Here we conduct a comprehensive series of semi-synthetic, biologically plausible, virtual interventional trials across 100M+ distinct simulations. We generate empirically grounded virtual trial data from large-scale meta-analytic connective, functional, genetic expression, and receptor distribution data, with high-resolution maps of 4K+ acute ischaemic lesions. Within each trial, we estimate treatment effects using models varying in complexity, in the presence of increasingly confounded outcomes and noisy treatment responses. Individualized prescriptions inferred from simple models, fitted to unconfounded data, were less accurate than those from complex models, fitted to confounded data. Our results indicate that complex modelling with richly represented lesion data is critical to individualized prescriptive inference in ischaemic stroke. |
| title | Individualized prescriptive inference in ischaemic stroke |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2301.10748 |