Factors associated with coronary artery disease treatment costs in India using linear, gamma, and quantile regression models
Year: 2026
Type: article
Study design: Observational Study
Abstract: Introduction : There is no uniformly agreed regression model for analysing cost data. The objective of the current study was to compare the performance of linear regression, gamma regression, and quantile regression and predict a better model using costs among patients with coronary artery disease (CAD). Methods A cross-sectional survey was conducted on CAD patients at a tertiary care hospital in New Delhi between May and October 2023. Descriptive statistics for direct and indirect costs were estimated, and their association with demographic and clinical variables was explored using linear, gamma and quantile regression along with prediction errors. Results 560 CAD patients were interviewed (mean age 62 ± 10.50years). 182 patients were found to be on medical therapy only, and 378 patients underwent any procedure along with medical treatment. The highest and lowest costs were observed for patients who underwent coronary artery bypass graft (US$3323/per patient) and received only medical therapy (US$220/per patient). According to linear regression, gender, distance to the hospital and type of intervention significantly affected the expenditure. In gamma regression, only the type of intervention was statistically significant. In quantile regression, being male, living away from the hospital and having a high SES score positively affected the expenditure. Quantile regression was found to have fewer prediction errors. Conclusion Establishing an appropriate statistical model is fundamental for predicting the costs, which could be affected by several factors, and the final choice of the regression model should be made after careful assessment of predictive ability and tailored to specific data.
Source: Discover Public Health
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: diamond
Funder: Assam University, Silchar
OpenAlex ID: W7172284301