Taille d'effet

Résumé

En statistique, une taille d'effet est une mesure de la force de l'effet observé d'une variable sur une autre et plus généralement d'une inférence. La taille d'un effet est donc une grandeur statistique descriptive calculée à partir de données observées empiriquement afin de fournir un indice quantitatif de la force de la relation entre les variables et non une statistique inférentielle qui permettrait de conclure ou non si ladite relation observée dans les données existe bien dans la réalité. En ce sens, la taille de l'effet est complémentaire à d'autres mesures statistiques telle que la valeur p d'un test t. Les mesures de taille d'effet sont particulièrement utiles pour conduire des méta-analyses qui exigent de comparer entre eux des résultats issus de différentes études scientifiques pour en faire la synthèse ou pour conduire des analyses de puissance destinées à établir si un protocole expérimental est adapté pour mesurer le phénomène que l'on cherche à étudier. Quelques exemple

Source officielle

https://fr.wikipedia.org/wiki/Taille_d'effet

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Publications associées (41)

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Concepts associés (24)

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Cours associés (18)

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Séances de cours associées (30)

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2004

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, , , ,

Background. Latencies of motor evoked potentials (MEPs) can provide insights into the motor neuronal pathways activated by transcranial magnetic stimulation. Notwithstanding its clinical relevance, accurate, unbiased methods to automatize latency detection are still missing. Objective. We present a novel open-source algorithm suitable for MEP onset/latency detection during resting state that only requires the post-stimulus electromyography signal and exploits the approximation of the first derivative of this signal to find the time point of initial deflection of the MEP. Approach. The algorithm has been benchmarked, using intra-class coefficient (ICC) and effect sizes, to manual detection of latencies done by three researchers independently on a dataset comprising almost 6500 MEP trials from healthy participants (n = 18) and stroke patients (n = 31) acquired during rest. The performance was further compared to currently available automatized methods, some of which created for active contraction protocols. Main results. The unstandardized effect size between the human raters and the present method is smaller than the sampling period for both healthy and pathological MEPs. Moreover, the ICC increases when the algorithm is added as a rater. Significance. The present algorithm is comparable to human expert decision and outperforms currently available methods. It provides a promising method for automated MEP latency detection under physiological and pathophysiological conditions.

IOP Publishing Ltd2022

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, , , , , , , ,

The development of novel strategies to augment motor training success is of great interest for healthy persons and neurological patients. A promising approach is the combination of training with transcranial electric stimulation. However, limited reproducibility and varying effect sizes make further protocol optimization necessary. We tested the effects of a novel cerebellar transcranial alternating current stimulation protocol (tACS) on motor skill learning. Furthermore, we studied underlying mechanisms by means of transcranial magnetic stimulation and analysis of fMRI-based resting-state connectivity. N=15 young, healthy participants were recruited. 50 Hz tACS was applied to the left cerebellum in a double-blind, sham-controlled, cross-over design concurrently to the acquisition of a novel motor skill. Potential underlying mechanisms were assessed by studying short intracortical inhibition at rest (SICIrest) and in the premovement phase (SICImove), intracortical facilitation at rest (ICFrest), and seed-based resting-state fMRI-based functional connectivity (FC) in a hypothesis-driven motor learning network. Active stimulation did not enhance skill acquisition or retention. Minor effects on striato-parietal FC were present. Linear mixed effects modelling identified SICImove modulation and baseline task performance as the most influential determining factors for predicting training success. Accounting for the identified factors may allow to stratify participants for future training-based interventions.

NATURE RESEARCH2020

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