DOI: 10.1101/19009159Oct 18, 2019Paper

Bringing Proportional Recovery into Proportion: Bayesian Hierarchical Modelling of Post-Stroke Motor Performance

MedRxiv : the Preprint Server for Health Sciences
Anna K BonkhoffH. Bowman

Abstract

Accurate predictions of motor performance after stroke are of cardinal importance for the patient, clinician, and health care system. More than ten years ago, the proportional recovery rule was introduced by promising just that: high-fidelity predictions of recovery following stroke based only on the initially lost motor performance, at least for a specific fraction of patients. However, emerging evidence suggests that this recovery rule is subject to various confounds and may apply less universally than assumed by many. We systematically revisited stroke outcome predictions by casting the data in a less confounded form and employing more integrative and flexible hierarchical Bayesian models. We jointly analyzed n=385 post-stroke trajectories from six separate studies - the currently largest overall dataset of upper limb motor recovery. We addressed confounding ceiling effects by introducing a subset approach and ensured correct model estimation through synthetic data simulations. Finally, we used model comparisons to assess the underlying nature of recovery within our empirical recovery data. The first model comparison, relying on the conventional fraction of patients called fitters, pointed to a combination of constant and pr...Continue Reading

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