scholarly journals Fitting the Fractional Polynomial Model to Non-Gaussian Longitudinal Data

2017 ◽  
Vol 8 ◽  
Author(s):  
Ji Hoon Ryoo ◽  
Jeffrey D. Long ◽  
Greg W. Welch ◽  
Arthur Reynolds ◽  
Susan M. Swearer
Keyword(s):  
Longitudinal Data ◽  
Polynomial Model ◽  
Fractional Polynomial ◽  
Non Gaussian

scholarly journals Limited Prognostic Utility of a Simplified Vital Sign Based Risk Calculator in Acute Medical Admissions

2020 ◽  
Vol 19 (3) ◽  
pp. 138-144
Author(s):  
Richard Conway ◽  
◽  
Declan Byrne ◽  
Deirdre O’Riordan ◽  
Bernard Silke ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Vital Sign ◽  
Illness Severity ◽  
Polynomial Model ◽  
Inhospital Mortality ◽  
Risk Calculator ◽  
Major Disease ◽  
Fractional Polynomial ◽  
Prognostic Utility ◽  
Vital Parameters

Background: Accurate efficient prognostication in acute medical admissions remains challenging.Methods: We constructed a Vital Sign based Risk Calculator using vital parameters and Major Disease Categories to predict 30-day in-hospital mortality using a multivariable fractional polynomial model. Results: We evaluated 113,807 admissions in 58,126 patients. The Vital Sign based Risk Calculator predicted 30-day inhospital mortality to increase from 2 points – 3.6% (95%CI 3.4, 3.7) to 12 points – 14.8% (95%CI 14.0, 15.7). AUROC was 0.74 (95%CI 0.72, 0.74). The addition of illness severity and comorbidity data improved AUROC to 0.90 (95%CI 0.89, 0.90). Conclusion: The Vital Sign based Risk Calculator is limited by its simplicity; inclusion of illness severity and comorbidity data improve prediction.

Estimation of extremes of non-Gaussian wind pressure on building roof: Sampling error in moment-based translation process model with no monotonic limit

2019 ◽  
Vol 23 (4) ◽  
pp. 810-826 ◽  
Author(s):  
Fengbo Wu ◽  
Min Liu ◽  
Qingshan Yang ◽  
Liuliu Peng
Keyword(s):  
Hermite Polynomial ◽  
Sampling Error ◽  
Polynomial Model ◽  
Wind Pressure ◽  
Transformation Model ◽  
Sampling Errors ◽  
Translation Process ◽  
Building Roof ◽  
Wind Pressures ◽  
Non Gaussian

Estimation of extremes of non-Gaussian wind pressure on building roof is necessary for cladding design. When limited length of non-Gaussian wind pressure is used for calculation, the estimated extreme involves sampling error. The moment-based Hermite polynomial model is extensively applied for estimation of extreme wind pressure due to the straightforwardness and accuracy, however, Hermite polynomial model has a monotonic limit resulting in a restricted application region of skewness and kurtosis combination. However, another two moment-based translation process models with no monotonic limit including Johnson transformation model and piecewise Hermite polynomial model have attracted some attention as these two models can be applied to a broader region of skewness and kurtosis combination. The sampling error in estimation of extremes of non-Gaussian wind pressure on building roof by Hermite polynomial model is proposed in the literature recently. Nevertheless, the sampling errors in Johnson transformation model and piecewise Hermite polynomial model have not been addressed. In this study, sampling errors in estimation of extremes of non-Gaussian wind pressures by Johnson transformation model are investigated. Formulations for estimating sampling errors of newly defined statistical moments and subsequent extremes in piecewise Hermite polynomial model are presented. The performance of sampling errors in Hermite polynomial model, Johnson transformation model, and piecewise Hermite polynomial model are finally compared with each other. Based on very long wind pressures from wind tunnel tests, it is shown that the sampling error of minimum wind pressure (suction) in Hermite polynomial model is generally the smallest compared to Johnson transformation model and piecewise Hermite polynomial model, while that of maximum wind pressure in piecewise Hermite polynomial model seems to be the smallest.

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