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

Haoyang Teng,Zhengjun Zhang

doi:10.3390/e23111517

Haoyang Teng, Zhengjun Zhang

Open Access

https://doi.org/10.3390/e23111517

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Abstract

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.

Highlights

Studying treatment effects is central in clinical trials and epidemiology
The purpose of the analysis using the logistic regression is to identify risk factors that are associated with the response variable of interests and the variables that influence the effect of exposure on disease and the risk factors
Motivated by the relative measurements, we propose a model that considers the relative treatment effects of the treatment groups in addition to the absolute treatment effects τj of the treatment groups in model (1)

Summary

Introduction

Studying treatment effects is central in clinical trials and epidemiology. When response variables are dichotomous, numerous applications of the logistic regression model can be found in the literature. If the primary goal is to measure the association between physical inactivity and heart disease with age being a confounding factor, the logistic regression is useful to model dichotomous variables (e.g., the values of 0 and 1 represent the status of heart disease, respectively), but it can be used to explain the effects of physical inactivity on heart disease while controlling for the age variable. Other applications can be found in genetics, clinical trials, or any studies that involve treatment groups. This statistical model has been a benchmark model due to its easy computability, interpretability, predictability, and stability (CIPS)

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