Data-Influence Analytics in Predictive Models Applied to Asthma Disease

Abstract

Asthma is one of the most common chronic diseases around the world and represents a serious problem in human health. Predictive models have become important in medical sciences because they provide valuable information for data-driven decision-making. In this work, a methodology of data-influence analytics based on mixed-effects logistic regression models is proposed for detecting potentially influential observations which can affect the quality of these models. Global and local influence diagnostic techniques are used simultaneously in this detection, which are often used separately. In addition, predictive performance measures are considered for this analytics. A study with children and adolescent asthma real data, collected from a public hospital of São Paulo, Brazil, is conducted to illustrate the proposed methodology. The results show that the influence diagnostic methodology is helpful for obtaining an accurate predictive model that provides scientific evidence when data-driven medical decision-making.

Predicting the Reputation of Pharmaceutical Firms with Financing and Geographical Location Data

“…The OR values can be significantly contrasted by the logistic regression model [72][73][74][75][76][77][78][79]. This model is a generalization of the classic linear regression model for dichotomic categorical dependent variables [73].…”

Log‐symmetric quantile regression models

“…However, it is an aspect to be developed and studied in the future. Although we considered a pseudo‐R 2 measure in order to select the best model, we understand that generalized leverage measures, the Cook distance, and global/local influence techniques (Tapia, Giampaoli, Leiva, & Lio, 2020; and Rocha et al, 2021) are important aspects to be taken into account in the statistical modeling. All of these and other aspects are part of future research.…”