Using beta regression modeling in medical sciences: a comparative study

: Beta regression (BR) models provide an adequate approach for modeling continuous outcomes of limited intervals (0, 1). The BR model assumes that the dependent variable follows a beta distribution and that its mean is affiliated to a set of exploratory variables through a linear predictor known as coefficients and link function. The BR model also includes a dispersion parameter. This paper describes the BR model along with its properties. Furthermore, the comparison between different link functions of the BR model is conducted through a medical real-life application

[1]  Z. Algamal,et al.  Modified jackknife ridge estimator for the Conway-Maxwell-Poisson model , 2023, Scientific African.

[2]  M. Abonazel,et al.  New Estimators for the Probit Regression Model with Multicollinearity , 2023, Scientific African.

[3]  M. Abonazel,et al.  Kibria–Lukman Estimator for the Conway–Maxwell Poisson Regression Model: Simulation and Applications , 2023, Scientific African.

[4]  Z. Algamal,et al.  New Two-Parameter Estimators for the Logistic Regression Model with Multicollinearity , 2022, WSEAS TRANSACTIONS ON MATHEMATICS.

[5]  M. Abonazel,et al.  A new Stein estimator for the zero‐inflated negative binomial regression model , 2022, Concurr. Comput. Pract. Exp..

[6]  Mohamed R. Abonazel,et al.  Beta ridge regression estimators: simulation and application , 2021, Commun. Stat. Simul. Comput..

[7]  H. Galal,et al.  Transperitoneal Laparoscopic Pyeloplasty versus Laparoscopic Assisted Pyeloplasty for the Treatment of Ureteropelvic Junction Obstruction in Children , 2020 .

[8]  D. Bandyopadhyay,et al.  Structured additive distributional zero augmented beta regression modeling of mortality in Nigeria. , 2020, Spatial statistics.

[9]  Raydonal Ospina,et al.  Model Selection Criteria on Beta Regression for Machine Learning , 2019, Mach. Learn. Knowl. Extr..

[10]  Fábio M. Bayer,et al.  Beta regression control chart for monitoring fractions and proportions , 2018, Comput. Ind. Eng..

[11]  Fang Liu,et al.  A review and comparison of Bayesian and likelihood-based inferences in beta regression and zero-or-one-inflated beta regression , 2018, Statistical methods in medical research.

[12]  Phillip Li,et al.  Efficient MCMC estimation of inflated beta regression models , 2018, Comput. Stat..

[13]  S. Aktas,et al.  Beta Regression for the Indicator Values of Well-Being Index For Provinces in Turkey , 2017 .

[14]  Diego Ramos Canterle,et al.  Variable dispersion beta regressions with parametric link functions , 2017, 1702.00327.

[15]  Raymond J. Taylor,et al.  Beta Regression Model for Predicting the Development of Pink Rot in Potato Tubers During Storage. , 2016, Plant disease.

[16]  Francisco Cribari‐Neto,et al.  Model selection criteria in beta regression with varying dispersion , 2014, Commun. Stat. Simul. Comput..

[17]  Francisco Cribari-Neto,et al.  Detecting Model Misspecification in Inflated Beta Regressions , 2014, Commun. Stat. Simul. Comput..

[18]  Vicente Núñez-Antón,et al.  Spatial Double Generalized Beta Regression Models , 2013 .

[19]  Francisco Cribari-Neto,et al.  Bartlett corrections in beta regression models , 2013, 1501.07551.

[20]  R. Adams,et al.  Application of Beta Regression to Analyze Ischemic Stroke Volume in NINDS rt-PA Clinical Trials , 2011, Neuroepidemiology.

[21]  S. Ferrari,et al.  Improved likelihood inference in beta regression , 2011 .

[22]  Raydonal Ospina,et al.  A general class of zero-or-one inflated beta regression models , 2011, Comput. Stat. Data Anal..

[23]  José M. R. Murteira,et al.  Alternative Estimating and Testing Empirical Strategies for Fractional Regression Models , 2011 .

[24]  A. Zeileis,et al.  Beta Regression in R , 2010 .

[25]  Roger Koenker,et al.  Parametric links for binary choice models: A Fisherian-Bayesian colloquy , 2009 .

[26]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[27]  Wagner Barreto-Souza,et al.  Improved estimators for a general class of beta regression models , 2008, Comput. Stat. Data Anal..

[28]  W. Johnson,et al.  BAYESIAN BETA REGRESSION: APPLICATIONS TO HOUSEHOLD EXPENDITURE DATA AND GENETIC DISTANCE BETWEEN FOOT‐AND‐MOUTH DISEASE VIRUSES , 2007 .

[29]  Raydonal Ospina,et al.  Inflated beta distributions , 2007, 0705.0700.

[30]  Francisco Cribari-Neto,et al.  Improved point and interval estimation for a beta regression model , 2006, Comput. Stat. Data Anal..

[31]  Michael Smithson,et al.  A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. , 2006, Psychological methods.

[32]  S. Ferrari,et al.  Beta Regression for Modelling Rates and Proportions , 2004 .

[33]  B. McCullough,et al.  Regression analysis of variates observed on (0, 1): percentages, proportions and fractions , 2003 .

[34]  J. Buckley Estimation of Models with Beta-Distributed Dependent Variables: A Replication and Extension of Paolino's Study , 2003, Political Analysis.

[35]  Francisco Cribari-Neto,et al.  Nearly Unbiased Maximum Likelihood Estimation for the Beta Distribution , 2002 .

[36]  R. Prentice,et al.  Commentary on Andersen and Gill's "Cox's Regression Model for Counting Processes: A Large Sample Study" , 1982 .

[37]  Víctor M. Guerrero,et al.  Use of the Box-Cox transformation with binary response models , 1982 .

[38]  Francisco J. Aranda-Ordaz,et al.  On Two Families of Transformations to Additivity for Binary Response Data , 1981 .

[39]  G. Weiss,et al.  Small sample comparison of estimation methods for the beta distribution , 1980 .

[40]  Francisco Cribari-Neto,et al.  Improved maximum likelihood estimation in a new class of beta regression models , 2005 .

[41]  Philip Paolino,et al.  Maximum Likelihood Estimation of Models with Beta-Distributed Dependent Variables , 2001, Political Analysis.