Titlebook: Quantile Regression in Clinical Research; Complete analysis fo Ton J. Cleophas,Aeilko H. Zwinderman Textbook 2021 The Editor(s) (if applica

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Intercept only Poisson Regression Versus Quantilele will be given from Kirkwood and Sterne (Standardization, in: Medical Statistics, Chap. 25, Blackwell Science, Oxford UK 2003) studied the age and sex adjusted mortality rate of onchocerciasis patients already blind, and the authors tested it versus non blind patients using Poisson regression. The

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Four Predictors Regressions Versus Quantile treatment predicted hours of sleep with a p-value of 0.02. In the multiple variables linear regression analysis the predictors group (treatment modality), age, gender (male/female), and co-morbidity were assessed. The predictors group (treatment modality) and age were significant at p?=?0.010 and p

虛度

Gene Expressions Regressions, Traditional Versus Quantilession showed that 6 genes were very significant independent predictors of drug efficacy scores. When tested against quantile regression, the results of the traditional multiple variables regression and the quantile regressions were pretty much similar, but the quantile regressions sometimes provided

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Laboratory Values Predict Survival Sepsis, Traditional Regression Versus Quantile effect of laboratory predictors on survival/septic death was assessed. Traditional predictors consisted of binary logistic regression with the logodds of survival from sepsis as outcome and the various laboratory values as predictors. Bilirubine, c-reactive protein, leucos are significant predictor

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Multinomial Regression Versus Quantileuces a lot of significant p-values and the investigators may rapidly be at a loss to know how to interpret them. Moreover, some readers may reject the multiple p-value approach and consider it a case of plenty type I errors of finding differences, where there are none. However, currently, test stati

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Traditional and Robust Regressions Versus Quantileression tends to give a better view of the relationships between predictor and outcome variables. In the example of this chapter quantile analysis not only provided better precision, but also a better insight into the relationship between the predictor and outcome variable.

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Continuous Trend Testing Versus Quantile Regression this chapter quantile analysis not only better precision, but also better insight into the relationship between the predictor and outcome variable was obtained with p-values of 0.001 and 0.0001 rather than 0.050.

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