scholarly journals Comparison of the Cox Semi-Parametric Model and Parametric Models in Analyzing the Effective Diagnostic Factors in Kidney Transplantation Survival

2019 ◽  
Author(s):  
Mohsen Askarishahi ◽  
Abdolamir Atapoor ◽  
Roya Hemayati ◽  
Shahrzad Shahidi ◽  
Sajedeh Zeynali
Keyword(s):  
Kidney Transplantation ◽  
Kidney Diseases ◽  
Survival Rates ◽  
Logistic Models ◽  
Information Criterion ◽  
Parametric Model ◽  
Parametric Models ◽  
Effective Factors ◽  
Transplanted Kidney ◽  
Discharge From Hospital

Introduction: Kidney transplantation is the best treatment for patients with advanced kidney diseases. The aim of this study was to determine the rate of transplanted kidney survival and compare the efficiency of Cox semi-parametric model with the parametric models in determination of survival effective factors. Method: This is a historic cohort study including the information of 381 ESRD patients, who underwent kidney transplant surgery from December 2007 to March 2016 in Noor hospital of Isfahan, Iran. In order to identify the effective factors in transplantation survival, the parametric and semi-parametric models were fitted with data and Akaike informationcriterion was used for detecting the most efficient model. Data analysis was carried out with R software, Version 3.1.0 at thesignificance level of 0.05. Results: According to the Kaplan-Mayer method, 1-, 3-, 5-, and 8-year survival rates of transplanted kidney were estimated as 0.987, 0.933, 0.869, and 0.839, respectively. Multi-variable analysis of all fitted models indicated that the duration of dialysis before transplantation (P ≤0.05) and the level of creatinine at the time of discharge from hospital (p≤0.05) had significant relationship with survival of transplanted kidney. Akaike values of Cox, Weibull, exponential, lognormal, and log-logistic models were calculated as 484, 484, 482, 484, and 356, respectively. Conclusion: Based on the Akaike information criterion, the Cox semi-parametric model was selected and proposed as the superior model.

Structural-parametric model of an electroelastic actuator for nanomechanics

2020 ◽  
pp. 3-11
Author(s):  
S.M. Afonin
Keyword(s):  
Transfer Function ◽  
Piezoelectric Actuator ◽  
Transfer Functions ◽  
Piezoelectric Effect ◽  
Parametric Model ◽  
Elastic Compliance ◽  
Parametric Models ◽  
Piezo Actuator ◽  
Structural Scheme ◽  
Model Transfer

Structural-parametric models, structural schemes are constructed and the transfer functions of electro-elastic actuators for nanomechanics are determined. The transfer functions of the piezoelectric actuator with the generalized piezoelectric effect are obtained. The changes in the elastic compliance and rigidity of the piezoactuator are determined taking into account the type of control. Keywords electro-elastic actuator, piezo actuator, structural-parametric model, transfer function, parametric structural scheme

scholarly journals Soluble urokinase-type plasminogen activator receptor (suPAR) is a risk indicator for eGFR loss in kidney transplant recipients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ulrich Jehn ◽  
Katharina Schütte-Nütgen ◽  
Ute Henke ◽  
Hermann Pavenstädt ◽  
Barbara Suwelack ◽  
...  
Keyword(s):  
Kidney Transplantation ◽  
Cox Regression ◽  
Kidney Diseases ◽  
Prognostic Significance ◽  
Risk Indicator ◽  
Kidney Transplant Recipients ◽  
Supar Level ◽  
Type Plasminogen Activator ◽  
Urokinase Type Plasminogen Activator ◽  
One Year

AbstractThe prognostic significance of suPAR in various kidney diseases has recently been demonstrated. Its role in transplantation-specific outcomes is still largely unknown. Therefore, we prospectively investigated the prognostic relevance of suPAR in patients before and one year after kidney transplantation (KTx). We included 100 patients who had received a kidney transplantation between 2013 and 2015. The plasma concentration of suPAR was measured by ELISA assay. In recipients of living donations (LD), pre-transplant suPAR levels were significantly lower than those of recipients of deceased donations (DD). After KTx, suPAR levels significantly declined in LD and DD recipients, without a detectable difference between both groups any more. Higher suPAR levels in recipients one year after KTx were associated with a more severe eGFR loss in the following three years in multivariable cox-regression (n = 82, p = 0.021). suPAR-levels above 6212 pg/ml one year after KTx are associated with eGFR loss > 30%, which occurred almost twice as fast as in patients with suPAR ≤ 6212 pg/ml (p < 0.001). Hence, suPAR level at one year mark might be a risk indicator of increased eGFR loss.

scholarly journals Development of strangulation ileus associated with the ureter of the transplanted kidney 18 years after kidney transplantation

2021 ◽  
Author(s):  
Toshihiro Shimizu ◽  
Saki Katano ◽  
Sho Nishida ◽  
Yoshitaka Kinoshita ◽  
Takahiro Shinzato ◽  
...  
Keyword(s):  
Kidney Transplantation ◽  
Transplanted Kidney ◽  
Strangulation Ileus

Beyond Deep Learning: An Econometric Example

2020 ◽  
Vol 28 (Supp01) ◽  
pp. 31-38 ◽  
Author(s):  
Ruofan Liao ◽  
Paravee Maneejuk ◽  
Songsak Sriboonchitta
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Prediction Models ◽  
Original Data ◽  
Parametric Model ◽  
Parametric Models ◽  
The Past ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Linear And Nonlinear

In the past, in many areas, the best prediction models were linear and nonlinear parametric models. In the last decade, in many application areas, deep learning has shown to lead to more accurate predictions than the parametric models. Deep learning-based predictions are reasonably accurate, but not perfect. How can we achieve better accuracy? To achieve this objective, we propose to combine neural networks with parametric model: namely, to train neural networks not on the original data, but on the differences between the actual data and the predictions of the parametric model. On the example of predicting currency exchange rate, we show that this idea indeed leads to more accurate predictions.

A Non Parametric Model for Magneto Rheological Dampers

1999 ◽  
Author(s):  
Mehdi Ahmadian ◽  
Xubin Song
Keyword(s):  
Test Data ◽  
Mr Damper ◽  
Parametric Model ◽  
Parametric Models ◽  
Magnetic Saturation ◽  
Mr Dampers ◽  
Electro Magnetic ◽  
Magneto Rheological ◽  
Force Characteristics ◽  
Non Parametric

Abstract A non-parametric model for magneto-rheological (MR) dampers is presented. After discussing the merits of parametric and non-parametric models for MR dampers, the test data for a MR damper is used to develop a non-parametric model. The results of the model are compared with the test data to illustrate the accuracy of the model. The comparison shows that the non-parametric model is able to accurately predict the damper force characteristics, including the damper non-linearity and electro-magnetic saturation. It is further shown that the parametric model can be numerically solved more efficiently than the parametric models.

scholarly journals Parametric and Semiparametric Estimations of Bivariate Truncated Type I Generalized Logistic Models driven from Copulas

2017 ◽  
Vol 7 (1) ◽  
pp. 72 ◽  
Author(s):  
Lamya A Baharith
Keyword(s):  
Logistic Models ◽  
Real Data ◽  
Information Criterion ◽  
Logistic Distribution ◽  
Type I ◽  
Copula Functions ◽  
Data Set ◽  
Monte Carlo Simulation Study ◽  
Generalized Logistic Distribution ◽  
Weibull Models

Truncated type I generalized logistic distribution has been used in a variety of applications. In this article, a new bivariate truncated type I generalized logistic (BTTGL) distributional models driven from three different copula functions are introduced. A study of some properties is illustrated. Parametric and semiparametric methods are used to estimate the parameters of the BTTGL models. Maximum likelihood and inference function for margin estimates of the BTTGL parameters are compared with semiparametric estimates using real data set. Further, a comparison between BTTGL, bivariate generalized exponential and bivariate exponentiated Weibull models is conducted using Akaike information criterion and the maximized log-likelihood. Extensive Monte Carlo simulation study is carried out for different values of the parameters and different sample sizes to compare the performance of parametric and semiparametric estimators based on relative mean square error.

scholarly journals The Parametric Model of the Human Mandible Coronoid Process Created by Method of Anatomical Features

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Nikola Vitković ◽  
Jelena Mitić ◽  
Miodrag Manić ◽  
Miroslav Trajanović ◽  
Karim Husain ◽  
...  
Keyword(s):  
3D Models ◽  
Parametric Model ◽  
Coronoid Process ◽  
Parametric Models ◽  
Specific Patient ◽  
Medical Practitioners ◽  
Anatomical Features ◽  
X Ray ◽  
Human Bones ◽  
Geometrical Data

Geometrically accurate and anatomically correct 3D models of the human bones are of great importance for medical research and practice in orthopedics and surgery. These geometrical models can be created by the use of techniques which can be based on input geometrical data acquired from volumetric methods of scanning (e.g., Computed Tomography (CT)) or on the 2D images (e.g., X-ray). Geometrical models of human bones created in such way can be applied for education of medical practitioners, preoperative planning, etc. In cases when geometrical data about the human bone is incomplete (e.g., fractures), it may be necessary to create its complete geometrical model. The possible solution for this problem is the application of parametric models. The geometry of these models can be changed and adapted to the specific patient based on the values of parameters acquired from medical images (e.g., X-ray). In this paper, Method of Anatomical Features (MAF) which enables creation of geometrically precise and anatomically accurate geometrical models of the human bones is implemented for the creation of the parametric model of the Human Mandible Coronoid Process (HMCP). The obtained results about geometrical accuracy of the model are quite satisfactory, as it is stated by the medical practitioners and confirmed in the literature.

scholarly journals The use of the nonlinear models in the growth of pears of ‘Shinseiki’ cultivar

2017 ◽  
Vol 48 (1) ◽  
Author(s):  
Thais Destefani Ribeiro ◽  
Taciana Villela Savian ◽  
Tales Jesus Fernandes ◽  
Joel Augusto Muniz
Keyword(s):  
Logistic Model ◽  
Least Squares Method ◽  
Nonlinear Models ◽  
Logistic Models ◽  
Information Criterion ◽  
Fruit Weight ◽  
Coefficient Of Determination ◽  
Pear Fruit ◽  
Asian Pear ◽  
Residual Correlations

ABSTRACT: The goal of this study was to elucidate the growth and development of the Asian pear fruit, on the grounds of length, diameter and fresh weight determined over time, using the non-linear Gompertz and Logistic models. The specifications of the models were assessed utilizing the R statistical software, via the least squares method and iterative Gauss-Newton process (DRAPER & SMITH, 2014). The residual standard deviation, adjusted coefficient of determination and the Akaike information criterion were used to compare the models. The residual correlations, observed in the data for length and diameter, were modeled using the second-order regression process to render the residuals independent. The logistic model was highly suitable in demonstrating the data, revealing the Asian pear fruit growth to be sigmoid in shape, showing remarkable development for three variables. It showed an average of up to 125 days for length and diameter and 140 days for fresh fruit weight, with values of 72mm length, 80mm diameter and 224g heavy fat.

scholarly journals Model Averaging with AIC Weights for Hypothesis Testing of Hormesis at Low Doses

Dose-Response ◽  
2017 ◽  
Vol 15 (2) ◽  
pp. 155932581771531
Author(s):  
Steven B. Kim ◽  
Nathan Sanders
Keyword(s):  
Hypothesis Testing ◽  
Dose Response ◽  
Statistical Power ◽  
Parametric Model ◽  
Semiparametric Models ◽  
Parametric Models ◽  
Large Sample Size ◽  
Low Doses ◽  
P Values ◽  
The Impact

For many dose–response studies, large samples are not available. Particularly, when the outcome of interest is binary rather than continuous, a large sample size is required to provide evidence for hormesis at low doses. In a small or moderate sample, we can gain statistical power by the use of a parametric model. It is an efficient approach when it is correctly specified, but it can be misleading otherwise. This research is motivated by the fact that data points at high experimental doses have too much contribution in the hypothesis testing when a parametric model is misspecified. In dose–response analyses, to account for model uncertainty and to reduce the impact of model misspecification, averaging multiple models have been widely discussed in the literature. In this article, we propose to average semiparametric models when we test for hormesis at low doses. We show the different characteristics of averaging parametric models and averaging semiparametric models by simulation. We apply the proposed method to real data, and we show that P values from averaged semiparametric models are more credible than P values from averaged parametric methods. When the true dose–response relationship does not follow a parametric assumption, the proposed method can be an alternative robust approach.

Optimal Parametric Model for Vortex Tube Using DOE

2011 ◽  
Author(s):  
Basel Alsayyed ◽  
Mohammad O. Hamdan ◽  
Emad Elnajjar
Keyword(s):  
Vortex Tube ◽  
Parametric Model ◽  
Geometric Parameters ◽  
Parametric Models ◽  
Outlet Temperature ◽  
Key Factors ◽  
Response Factors ◽  
Outlet Flow ◽  
Mass Flow Rates ◽  
Heating Load

In this study, a vortex tube geometric parametric model will be developed and the parameters will be considered as factors that affect the performance of a vortex tube. SolidWorks is used to generate parametric models; Minitab is used for Design Of Experiments (DOE) combination setups. A 3D printer is used to produce a physical model of the vortex tube to fit each of the DOE combinations. The study reports the effect of different geometric parameters on the cooling/heating load and the outlet temperature. The geometric parameters are investigated by measuring temperatures, pressures and mass flow rates for the inlet and hot/cold outlet flow. Two key factors were considered, namely mass fraction and angle of nozzle. Response factors analyzed are the maximum hot temperature (THMax) and the minimum cold temperature (TCMin).

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