Cote Z (statistiques)

Résumé

La cote Z correspond au nombre d'écarts types séparant un résultat de la moyenne. Au Québec, cette cote était la cote principalement utilisée pour évaluer le rendement des étudiants collégiaux par les universités. Elle existe toujours en tant que composante de la cote R. Évaluation La cote Z se calcule de la même façon que la variable centrée réduite : {Cote Z=}\frac{X-\mu{}}{\sigma} où

{Cote Z} : différence entre le résultat et la moyenne, divisé par l'écart-type *X : valeur *\mu{} : Moyenne du groupe *\sigma{} : Écart type du groupe

Historique Les universités du Québec utilisaient la cote Z jusqu'en 1994 pour sélectionner les étudiants. Fondée uniquement sur la force de l'étudiant par rapport à son groupe, elle ne prenait pas en considération la force relative de ce dernier par rapport aux autres groupes. La cote R a été instaurée pour corriger cette lacune avec d'autres paramètres. N

Source officielle

https://fr.wikipedia.org/wiki/Cote_Z_(statistiques)

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Microarray gene expression data sets are jointly analyzed to increase statistical power. They could either be merged together or analyzed by meta-analysis. For a given ensemble of data sets, it cannot be foreseen which of these paradigms, merging or meta-analysis, works better. In this article, three joint analysis methods, Z-score normalization, ComBat and the inverse normal method (meta-analysis) were selected for survival prognosis and risk assessment of breast cancer patients. The methods were applied to eight microarray gene expression data sets, totaling 1324 patients with two clinical endpoints, overall survival and relapse-free survival. The performance derived from the joint analysis methods was evaluated using Cox regression for survival analysis and independent validation used as bias estimation. Overall, Z-score normalization had a better performance than ComBat and meta-analysis. Higher Area Under the Receiver Operating Characteristic curve and hazard ratio were also obtained when independent validation was used as bias estimation. With a lower time and memory complexity, Z-score normalization is a simple method for joint analysis of microarray gene expression data sets. The derived findings suggest further assessment of this method in future survival prediction and cancer classification applications.

Oxford Univ Press2016

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Quantitative brain relaxation atlases for personalized detection and characterization of brain pathology

Guillaume Bonnier, Cristina Granziera, Tom Hilbert, Tobias Kober, Reto Meuli, Gian Franco Piredda, Jean-Philippe Thiran

Purpose To exploit the improved comparability and hardware independency of quantitative MRI, databases of MR physical parameters in healthy tissue are required, to which tissue properties of patients can be compared. In this work, normative values for longitudinal and transverse relaxation times in the brain were established and tested in single-subject comparisons for detection of abnormal relaxation times. Methods Relaxometry maps of the brain were acquired from 52 healthy volunteers. After spatially normalizing the volumes into a common space, T-1 and T-2 inter-subject variability within the healthy cohort was modeled voxel-wise. A method for a single-subject comparison against the atlases was developed by computing z-scores with respect to the established healthy norms. The comparison was applied to two multiple sclerosis and one clinically isolated syndrome cases for a proof of concept. Results The established atlases exhibit a low variation in white matter structures (median RMSE of models equal to 32 ms for T-1 and 4 ms for T-2), indicating that relaxation times are in a narrow range for normal tissues. The proposed method for single-subject comparison detected relaxation time deviations from healthy norms in the example patient data sets. Relaxation times were found to be increased in brain lesions (mean z-scores >5). Moreover, subtle and confluent differences (z-scores similar to 2-4) were observed in clinically plausible regions (between lesions, corpus callosum). Conclusions Brain T-1 and T-2 quantitative norms were derived voxel-wise with low variability in healthy tissue. Example patient deviation maps demonstrated good sensitivity of the atlases for detecting relaxation time alterations.

WILEY2019

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Effect heterogeneity and variable selection for standardizing causal effects to a target population

Mats Julius Stensrud

The participants in randomized trials and other studies used for causal inference are often not representative of the populations seen by clinical decision-makers. To account for differences between populations, researchers may consider standardizing results to a target population. We discuss several different types of homogeneity conditions that are relevant for standardization: Homogeneity of effect measures, homogeneity of counterfactual outcome state transition parameters, and homogeneity of counterfactual distributions. Each of these conditions can be used to show that a particular standardization procedure will result in an unbiased estimate of the effect in the target population, given assumptions about the relevant scientific context. We compare and contrast the homogeneity conditions, in particular their implications for selection of covariates for standardization and their implications for how to compute the standardized causal effect in the target population. While some of the recently developed counterfactual approaches to generalizability rely upon homogeneity conditions that avoid many of the problems associated with traditional approaches, they often require adjustment for a large (and possibly unfeasible) set of covariates.

2019

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