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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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A collection of parametric modal regression models for bounded data.

André F B Menezes¹, Josmar Mazucheli², Subrata Chakraborty³

¹Departamento De Estatística, Universidade Estadual De Campinas, Campinas, Brasil.

Journal of Biopharmaceutical Statistics

|May 31, 2021

Summary

Modal regression offers a novel way to analyze bounded data, uncovering patterns missed by standard methods. This study introduces new parametric models and an R package for broader application in health and social sciences.

Keywords:

Beta distribution Kumaraswamy distribution Parametric modal regression

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Area of Science:

Statistics
Biostatistics
Econometrics

Background:

Traditional regression methods may overlook crucial information in the data's central tendency.
Understanding the relationship between covariates and the most likely response is vital in many scientific fields.
Bounded response variables are common in health and social sciences, requiring specialized modeling techniques.

Purpose of the Study:

To introduce a collection of parametric modal regression models tailored for bounded response variables.
To explore and compare the properties of recently introduced and established probability distributions for modal regression.
To provide an accessible R package for implementing these novel modal regression models.

Main Methods:

Development of parametric modal regression models utilizing probability distributions with bounded support.
Inclusion of well-established Beta and Kumaraswamy distributions alongside newer distributions.
Empirical validation using three real-world datasets from health and social science domains.

Main Results:

The study highlights key properties and comparative analyses of the proposed modal regression models.
Empirical results demonstrate the effectiveness of modal regression in analyzing health and social science data.
The proposed models reveal important structures potentially missed by conventional regression approaches.

Conclusions:

Modal regression provides a valuable alternative for analyzing bounded response variables.
The newly developed models and R package enhance the toolkit for researchers in statistics and related fields.
Accessible implementation through the unitModalReg R package promotes reproducible research and wider adoption.