scholarly journals Decision curve analysis apropos of choice of preferable treatment positioning during breast irradiation

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Ferenc Rárosi ◽  
Krisztina Boda ◽  
Zsuzsanna Kahán ◽  
Zoltán Varga
Keyword(s):  
Logistic Regression ◽  
Linear Regression ◽  
Regression Model ◽  
Prone Position ◽  
Linear Regression Model ◽  
Gold Standard ◽  
Cross Validation ◽  
Breast Irradiation ◽  
Wide Range ◽  
Late Morbidity

Abstract Background Radiotherapy is a standard treatment option for breast cancer, but it may lead to significant late morbidity, including radiation heart damage. Breast irradiation performed individually in the supine or prone position may aid in minimizing the irradiation dose to the heart and LAD coronary artery. A series of CT scans and therapy plans are needed in both positions for the ‘gold standard’ decision on the preferable treatment position. This method is expensive with respect to technology and physician workload. Our ultimate goal is to develop a predictive tool to identify the preferable treatment position using easily measurable patient characteristics. In this article, we describe the details of how model building and consequently validation of the best model are done. Methods Different models were used: both logistic regression and multiple linear regressions were used to estimate the LAD mean dose difference (the difference between the mean dose to the LAD in the supine position versus prone position); predicted dose differences were analysed compared to the ‘gold standard’ values, and the best model was selected accordingly. The final model was checked by random cross-validation. In addition to generally used measures (ROC and Brier score), decision curves were employed to evaluate the performance of the models. Results ROC analysis demonstrated that none of the predictors alone was satisfactory. Multiple logistic regression models and the linear regression model lead to high values of net benefit for a wide range of threshold probabilities. Multiple linear regression seemed to be the most useful model. We also present the results of the random cross-validation for this model (i.e. sensitivity of 80.7% and specificity of 87.5%). Conclusions Decision curves proved to be useful to evaluate our models. Our results indicate that any of the models could be implemented in clinical practice, but the linear regression model is the most useful model to facilitate the radiation treatment decision. In addition, it is in use in everyday practice in the Department of Oncotherapy, University of Szeged, Hungary.

scholarly journals Quality of Care in Long-Term Hemodialysis Patients in Mexico

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Torres-Díaz JA ◽  
◽  
Gonzalez-Gonzalez JG ◽  
Zúniga-Hernández JA ◽  
Olivo-Gutiérrez MC ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Quality Of Care ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
World Literature ◽  
Logistic Regression Model ◽  
Hospital Admissions ◽  
Cross Sectional

Introduction: The End Stage Renal Disease (ESRD) is one of the leading causes of mortality in Mexico. The quality of care these patients receive remains uncertain. Methods: This is a descriptive, single-center and cross-sectional cohort study. The KDOQI performance measures, hemoglobin level >11 g/dL, blood pressure <140/90 mmHg, serum albumin >4 g/dL and use of arteriovenous fistula of patients with ESRD on hemodialysis were analyzed in a period of a year. The association between mortality and the KDOQI objectives was evaluated with a logistic regression model. A linear regression model was also performed with the number of readmissions. Results: A total of 124 participants were included. Participants were categorized by the number of measures completed. Fourteen (11.3%) of the participants did not meet any of the goals, 51 (41.1%) met one, 43 (34.7%) met two, 11 (8.9%) met three, and 5 (4%) met the four clinical goals analyzed. A mortality of 11.2% was registered. In the logistic regression model, the number of goals met had an OR for mortality of 1.1 (95% CI 0.5-2.8). In the linear regression model, for the number of readmissions, a beta correlation with the number of KDOQI goals met was 0.246 (95% CI -0.872-1.365). Conclusion: The attainment of clinical goals and the mortality rate in our center is similar to that reported in the world literature. Our study did not find a significant association between compliance with clinical guidelines and mortality or the number of hospital admissions in CKD patients on hemodialysis.

Ordinal regression model and the linear regression model were superior to the logistic regression models

2006 ◽  
Vol 59 (5) ◽  
pp. 448-456 ◽  
Author(s):  
Colleen M. Norris ◽  
William A. Ghali ◽  
L. Duncan Saunders ◽  
Rollin Brant ◽  
Diane Galbraith ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Models ◽  
Ordinal Regression ◽  
Logistic Regression Models ◽  
Ordinal Regression Model

scholarly journals Solving Multicollinearity Problem in Linear Regression Model: The Review Suggests New Idea of Partitioning and Extraction of the Explanatory Variables

2021 ◽  
Vol 2 (1) ◽  
pp. 12-20
Author(s):  
Kayode Ayinde, Olusegun O. Alabi ◽  
Ugochinyere Ihuoma Nwosu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Real Data ◽  
Least Square ◽  
Model Parameters ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
Wide Range ◽  
High Level

Multicollinearity has remained a major problem in regression analysis and should be sustainably addressed. Problems associated with multicollinearity are worse when it occurs at high level among regressors. This review revealed that studies on the subject have focused on developing estimators regardless of effect of differences in levels of multicollinearity among regressors. Studies have considered single-estimator and combined-estimator approaches without sustainable solution to multicollinearity problems. The possible influence of partitioning the regressors according to multicollinearity levels and extracting from each group to develop estimators that will estimate the parameters of a linear regression model when multicollinearity occurs is a new econometrics idea and therefore requires attention. The results of new studies should be compared with existing methods namely principal components estimator, partial least squares estimator, ridge regression estimator and the ordinary least square estimators using wide range of criteria by ranking their performances at each level of multicollinearity parameter and sample size. Based on a recent clue in literature, it is possible to develop innovative estimator that will sustainably solve the problem of multicollinearity through partitioning and extraction of explanatory variables approaches and identify situations where the innovative estimator will produce most efficient result of the model parameters. The new estimator should be applied to real data and popularized for use.

ASYMPTOTIC THEORY FOR M-ESTIMATORS OVER A CONVEX KERNEL

1998 ◽  
Vol 14 (4) ◽  
pp. 387-422 ◽  
Author(s):  
Miguel A. Arcones
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Limit Distribution ◽  
Linear Regression Model ◽  
Asymptotic Theory ◽  
Multivariate Linear Regression ◽  
Multivariate Linear Regression Model ◽  
Taylor Expansions ◽  
Wide Range ◽  
Different Types

We study the convergence in distribution of M-estimators over a convex kernel. Under convexity, the limit distribution of M-estimators can be obtained under minimal assumptions. We consider the case when the limit is arbitrary, not necessarily normal. If some Taylor expansions hold, the limit distribution is stable. As an application, we examine the limit distribution of M-estimators for the multivariate linear regression model. We obtain the distributional convergence of M-estimators for the multivariate linear regression model for a wide range of sequences of regressors and different types of conditions on the sequence of errors.

Characterizing the Conductivity of Aerosol Jet Printed Silver Features on Glass

2021 ◽  
Author(s):  
Dilan Ratnayake ◽  
Alexander Thomas Curry ◽  
Chuang Qu ◽  
John Usher ◽  
Kevin Walsh
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Narrow Range ◽  
Model Development ◽  
Post Processing ◽  
Printable Electronics ◽  
Wide Range ◽  
Jet Printing ◽  
Aerosol Jet Printing

Abstract Aerosol Jet Printing shows a lot of promise for the future of printable electronics. It is compatible with a wide range of materials and can be printed on nearly any type of surface features because of its 3–5 mm standoff distance from the substrate. However, nearly all materials printed require some form of post-sintering processing to reduce the electrical resistance. Many companies develop these materials, but only provide a narrow range of post processing results to demonstrate the achievable conductivity values. In this paper, a design of experiment (DOE) is presented that demonstrates a way to characterize any material for Aerosol Jet Printing during and after post sintering processing by measuring conductivity with different time and temperature values. From these results, a linear regression model can be made to develop an equation that predicts conductivity at a given time-temperature value. This paper applies this method to Clariant Ag ink and sinters silver pads in an oven. A linear regression model is successfully developed that fits the data very well. From this model, an equation is derived to predict the conductivity of the Clariant Ag ink for any time-temperature value. Although only demonstrated with an oven and one type of ink, this method of experimentation and model development can be done with any material and any post processing method.

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