On a new piecewise regression model with cure rate: Diagnostics and application to medical data

2021 ◽  
Author(s):  
Yolanda M. Gómez ◽  
Diego I. Gallardo ◽  
Jeremias Leão ◽  
Vinicius F. Calsavara
Keyword(s):  
Regression Model ◽  
Medical Data ◽  
Cure Rate ◽  
Piecewise Regression

Hazard regression model and cure rate model in colon cancer relative survival trends: are they telling the same story?

2008 ◽  
Vol 23 (4) ◽  
pp. 251-259 ◽  
Author(s):  
Theodora Bejan-Angoulvant ◽  
Anne-Marie Bouvier ◽  
Nadine Bossard ◽  
Aurelien Belot ◽  
Valérie Jooste ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Regression Model ◽  
Relative Survival ◽  
Rate Model ◽  
Cure Rate ◽  
Hazard Regression ◽  
Cure Rate Model

FUZZY MULTIPLE OBJECTIVE PROGRAMMING IN AN INTERVAL PIECEWISE REGRESSION MODEL

2009 ◽  
Vol 17 (03) ◽  
pp. 365-376 ◽  
Author(s):  
JING-RUNG YU ◽  
GWO-HSHIUNG TZENG
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Heuristic Approach ◽  
Multiple Objective Programming ◽  
Change Points ◽  
Multiple Objective ◽  
Compromise Solution ◽  
Numerical Examples ◽  
Piecewise Regression ◽  
Objective Programming

This study proposes fuzzy multiple objective programming to determine the measure of fitness and the number of change-points in an interval piecewise regression model. To increase the measure of fitness, Tanaka and Lee proposed a conceptual procedure, which is a heuristic approach and becomes complicated for determining the proper polynomial. Therefore, a multiple objective approach is adopted to obtain a compromise solution among three objectives — maximizing the measure of fitness, minimizing the number of change-points and minimizing the width to obtain the interval regression models. By using the proposed method, a better measure of fitness can be obtained. Two numerical examples are used as demonstrations to illustrate our approach in more detail.

scholarly journals Predicting the cure rate of breast cancer using a new regression model with four regression structures

2017 ◽  
Vol 27 (11) ◽  
pp. 3207-3223 ◽  
Author(s):  
Thiago G Ramires ◽  
Gauss M Cordeiro ◽  
Michael W Kattan ◽  
Niel Hens ◽  
Edwin MM Ortega
Keyword(s):  
Breast Carcinoma ◽  
Regression Model ◽  
Additive Model ◽  
Real Data ◽  
Cure Rate ◽  
Time Model ◽  
Data Set ◽  
Explanatory Variables ◽  
Diagnostic Measures ◽  
Cure Fraction

Cure fraction models are useful to model lifetime data with long-term survivors. We propose a flexible four-parameter cure rate survival model called the log-sinh Cauchy promotion time model for predicting breast carcinoma survival in women who underwent mastectomy. The model can estimate simultaneously the effects of the explanatory variables on the timing acceleration/deceleration of a given event, the surviving fraction, the heterogeneity, and the possible existence of bimodality in the data. In order to examine the performance of the proposed model, simulations are presented to verify the robust aspects of this flexible class against outlying and influential observations. Furthermore, we determine some diagnostic measures and the one-step approximations of the estimates in the case-deletion model. The new model was implemented in the generalized additive model for location, scale and shape package of the R software, which is presented throughout the paper by way of a brief tutorial on its use. The potential of the new regression model to accurately predict breast carcinoma mortality is illustrated using a real data set.

INTERVAL PIECEWISE REGRESSION MODEL WITH AUTOMATIC CHANGE-POINT DETECTION BY QUADRATIC PROGRAMMING

2005 ◽  
Vol 13 (03) ◽  
pp. 347-361 ◽  
Author(s):  
JING-RUNG YU ◽  
GWO-HSHIUNG TZENG ◽  
HAN-LIN LI
Keyword(s):  
Linear Programming ◽  
Quadratic Programming ◽  
Regression Model ◽  
Change Point ◽  
Regression Models ◽  
Change Point Detection ◽  
Fuzzy Regression ◽  
Programming Approach ◽  
Piecewise Regression ◽  
Point Detection

To handle large variation data, an interval piecewise regression method with automatic change-point detection by quadratic programming is proposed as an alternative to Tanaka and Lee's method. Their unified quadratic programming approach can alleviate the phenomenon where some coefficients tend to become crisp in possibilistic regression by linear programming and also obtain the possibility and necessity models at one time. However, that method can not guarantee the existence of a necessity model if a proper regression model is not assumed especially with large variations in data. Using automatic change-point detection, the proposed method guarantees obtaining the necessity model with better measure of fitness by considering variability in data. Without piecewise terms in estimated model, the proposed method is the same as Tanaka and Lee's model. Therefore, the proposed method is an alternative method to handle data with the large variations, which not only reduces the number of crisp coefficients of the possibility model in linear programming, but also simultaneously obtains the fuzzy regression models, including possibility and necessity models with better fitness. Two examples are presented to demonstrate the proposed method.

scholarly journals Directly and Simultaneously Expressing Absolute and Relative Treatment Effects in Medical Data Models and Applications

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1517
Author(s):  
Hao Yang Teng ◽  
Zhengjun Zhang
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Treatment Effects ◽  
Real Data ◽  
Medical Data ◽  
Severe Problem ◽  
New Model ◽  
Logistic Regression Models

Logistic regression is widely used in the analysis of medical data with binary outcomes to study treatment effects through (absolute) treatment effect parameters in the models. However, the indicative parameters of relative treatment effects are not introduced in logistic regression models, which can be a severe problem in efficiently modeling treatment effects and lead to the wrong conclusions with regard to treatment effects. This paper introduces a new enhanced logistic regression model that offers a new way of studying treatment effects by measuring the relative changes in the treatment effects and also incorporates the way in which logistic regression models the treatment effects. The new model, called the Absolute and Relative Treatment Effects (AbRelaTEs) model, is viewed as a generalization of logistic regression and an enhanced model with increased flexibility, interpretability, and applicability in real data applications than the logistic regression. The AbRelaTEs model is capable of modeling significant treatment effects via an absolute or relative or both ways. The new model can be easily implemented using statistical software, with the logistic regression model being treated as a special case. As a result, the classical logistic regression models can be replaced by the AbRelaTEs model to gain greater applicability and have a new benchmark model for more efficiently studying treatment effects in clinical trials, economic developments, and many applied areas. Moreover, the estimators of the coefficients are consistent and asymptotically normal under regularity conditions. In both simulation and real data applications, the model provides both significant and more meaningful results.

Piecewise Regression Model for Solenoid Embedded Inductors Based on the Quasi-newton Method

2005 ◽  
Vol 6 (6) ◽  
pp. 256-261
Author(s):  
Young-Don Ko ◽  
Kil-Han Kim ◽  
Il-Gu Yun ◽  
Kyu-Bok Lee ◽  
Jong-Kyu Kim
Keyword(s):  
Regression Model ◽  
Newton Method ◽  
Piecewise Regression ◽  
Quasi Newton

Incorporation of frailties into a cure rate regression model and its diagnostics and application to melanoma data

2018 ◽  
Vol 37 (29) ◽  
pp. 4421-4440 ◽  
Author(s):  
Jeremias Leão ◽  
Víctor Leiva ◽  
Helton Saulo ◽  
Vera Tomazella
Keyword(s):  
Regression Model ◽  
Cure Rate

scholarly journals Negative Binomial Kumaraswamy-G Cure Rate Regression Model

2018 ◽  
Vol 11 (1) ◽  
pp. 6 ◽  
Author(s):  
Amanda D’Andrea ◽  
Ricardo Rocha ◽  
Vera Tomazella ◽  
Francisco Louzada
Keyword(s):  
Regression Model ◽  
Negative Binomial ◽  
Cure Rate
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