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@article{188905,
        author = {Vijayan S and Kavitha S},
        title = {Estimating parametric survival distributions for stroke outcomes using statistical and graphical diagnostics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {4165-4169},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188905},
        abstract = {This study examines the performance of Weibull, Gamma, Log-logistic, and Lognormal distributions when applied to survival time data of stroke patients. Model performance was assessed using log-likelihood, AIC, BIC, and visual diagnostics, including PP plots, QQ plots, and fitted density and cumulative distribution curves. The comparative analysis identifies the distribution that best captures the observed survival pattern, providing a robust foundation for risk assessment and survival prediction. Overall, the results highlight the importance of selecting models that closely match the empirical hazard behavior and reinforce the relevance of parametric survival modeling in clinical studies.},
        keywords = {Parameter model, log-likelihood, Akaike Information Criterion, Bayesian Information Criterion, Survival analysis, real-life data.},
        month = {December},
        }