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ISMOD: an all-subsets regression program for generalized linear models. I. Statistical and computational background.

J F Lawless, K Singhal

    Computer Methods and Programs in Biomedicine
    |April 1, 1987
    PubMed
    Summary
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    This study introduces a new system for regression analysis in biomedical research, covering generalized linear models for continuous and discrete data. The system aids in model fitting, diagnostics, and offers an all-subsets regression feature for comprehensive analysis.

    Area of Science:

    • Biomedical Research
    • Statistical Modeling

    Background:

    • Generalized linear models are crucial in biomedical research for analyzing various data types.
    • Existing systems may lack comprehensive features for complex regression analyses.

    Purpose of the Study:

    • To describe a novel system designed for regression analyses within generalized linear models.
    • To implement and apply models widely used in biomedical research, including survival and discrete response models.

    Main Methods:

    • The system supports continuous response models like Weibull, log-logistic, log-normal, and Cox proportional hazards.
    • It also accommodates discrete response models including Poisson, binomial, and multinomial regression.

    Main Results:

    • The system facilitates model fitting and generates essential diagnostic outputs, such as residuals.

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  • An all-subsets regression feature is included for exhaustive model exploration.

    • Conclusions:

    • The described system provides a robust tool for advanced regression analyses in biomedical fields.
    • This system enhances the capability to perform detailed statistical analyses on diverse biomedical datasets.