{"title":"A New Regression Model for Analysis of Count Response and Other Related Patient Outcomes","authors":"Khawar Javaid, Bilal Ahmad Para","doi":"10.1007/s40745-025-00620-0","DOIUrl":null,"url":null,"abstract":"<div><p>This study introduces a novel statistical model, termed New Poisson Generalized Lindley (NP-GLindley) distribution, for analysing the count response variable and various associated patient outcomes. The model combines the advantages of the Poisson distribution and a generalisation of the Lindley distribution, offering a flexible approach for count data analysis. The structural characteristics of the presented discrete model have been thoroughly examined. The results demonstrate the efficacy of the NP-GLindley model in identifying the intricacies of count data associated with the healthcare domain. The new model outperforms traditional count regression models in terms of goodness of fit and predictive accuracy and establishes itself as a feasible alternative in case of over-dispersion as well. The proposed model emerges as a promising tool for researchers and practitioners for conducting analysis of healthcare related count data.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"13 5","pages":"1233 - 1258"},"PeriodicalIF":0.0000,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Data Science","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s40745-025-00620-0","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Decision Sciences","Score":null,"Total":0}
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Abstract
This study introduces a novel statistical model, termed New Poisson Generalized Lindley (NP-GLindley) distribution, for analysing the count response variable and various associated patient outcomes. The model combines the advantages of the Poisson distribution and a generalisation of the Lindley distribution, offering a flexible approach for count data analysis. The structural characteristics of the presented discrete model have been thoroughly examined. The results demonstrate the efficacy of the NP-GLindley model in identifying the intricacies of count data associated with the healthcare domain. The new model outperforms traditional count regression models in terms of goodness of fit and predictive accuracy and establishes itself as a feasible alternative in case of over-dispersion as well. The proposed model emerges as a promising tool for researchers and practitioners for conducting analysis of healthcare related count data.
期刊介绍：
Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed. ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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