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

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 ca...

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Autores principales:	Haoyang Teng, Zhengjun Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	asymptotics enhanced logistic regression estimator consistency interpretability precision medicine predictability Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/5f43893d667f40dca24438f66d065037
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spelling	oai:doaj.org-article:5f43893d667f40dca24438f66d0650372021-11-25T17:30:26ZDirectly and Simultaneously Expressing Absolute and Relative Treatment Effects in Medical Data Models and Applications10.3390/e231115171099-4300https://doaj.org/article/5f43893d667f40dca24438f66d0650372021-11-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1517https://doaj.org/toc/1099-4300Logistic 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.Haoyang TengZhengjun ZhangMDPI AGarticleasymptoticsenhanced logistic regressionestimator consistencyinterpretabilityprecision medicinepredictabilityScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1517, p 1517 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	asymptotics enhanced logistic regression estimator consistency interpretability precision medicine predictability Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	asymptotics enhanced logistic regression estimator consistency interpretability precision medicine predictability Science Q Astrophysics QB460-466 Physics QC1-999 Haoyang Teng Zhengjun Zhang Directly and Simultaneously Expressing Absolute and Relative Treatment Effects in Medical Data Models and Applications
description	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.
format	article
author	Haoyang Teng Zhengjun Zhang
author_facet	Haoyang Teng Zhengjun Zhang
author_sort	Haoyang Teng
title	Directly and Simultaneously Expressing Absolute and Relative Treatment Effects in Medical Data Models and Applications
title_short	Directly and Simultaneously Expressing Absolute and Relative Treatment Effects in Medical Data Models and Applications
title_full	Directly and Simultaneously Expressing Absolute and Relative Treatment Effects in Medical Data Models and Applications
title_fullStr	Directly and Simultaneously Expressing Absolute and Relative Treatment Effects in Medical Data Models and Applications
title_full_unstemmed	Directly and Simultaneously Expressing Absolute and Relative Treatment Effects in Medical Data Models and Applications
title_sort	directly and simultaneously expressing absolute and relative treatment effects in medical data models and applications
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5f43893d667f40dca24438f66d065037
work_keys_str_mv	AT haoyangteng directlyandsimultaneouslyexpressingabsoluteandrelativetreatmenteffectsinmedicaldatamodelsandapplications AT zhengjunzhang directlyandsimultaneouslyexpressingabsoluteandrelativetreatmenteffectsinmedicaldatamodelsandapplications
_version_	1718412286553489408

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

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