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ISMOD: an all-subsets regression program for generalized linear models. II. Program guide and examples.

J F Lawless, K Singhal

    Computer Methods and Programs in Biomedicine
    |April 1, 1987
    PubMed
    Summary
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    This paper introduces the ISMOD system for generalized linear model regression analysis, widely used in biomedical research for survival and discrete response models. It offers model fitting, diagnostics, and all-subsets regression capabilities.

    Area of Science:

    • Biostatistics
    • Biomedical Data Analysis

    Background:

    • Generalized linear models (GLMs) are fundamental in biomedical research for analyzing diverse data types.
    • Commonly used models include survival analysis (Weibull, log logistic, log normal, Cox proportional hazards) and discrete response models (Poisson, binomial, multinomial).

    Purpose of the Study:

    • To describe the ISMOD system, a software tool designed for comprehensive regression analyses within GLMs.
    • To provide statistical background and demonstrate the application of ISMOD in biomedical research.

    Main Methods:

    • Implementation of various continuous and discrete response regression models within the ISMOD system.
    • Development of features for model fitting, residual generation, diagnostic output, and all-subsets regression.

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    Main Results:

    • The ISMOD system successfully implements a range of widely used generalized linear models.
    • The system provides essential tools for model diagnostics and exploratory analysis, including an all-subsets regression feature.

    Conclusions:

    • The ISMOD system offers a versatile platform for advanced regression analysis in biomedical research.
    • Its comprehensive features facilitate robust model fitting and diagnostic evaluation for various data types.