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A computer program for stepwise logistic regression using maximum likelihood estimation.

D W Hosmer, C Y Wang, I C Lin

    Computer Programs in Biomedicine
    |June 1, 1978
    PubMed
    Summary
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    This study introduces a computer program for stepwise logistic regression variable selection. It uses likelihood ratio tests for efficient model building, demonstrated with examples.

    Area of Science:

    • Statistics
    • Computational Biology
    • Biostatistics

    Background:

    • Logistic regression is a widely used statistical method for modeling binary outcomes.
    • Variable selection is crucial for developing parsimonious and interpretable logistic regression models.
    • Automated variable selection methods can improve efficiency and reduce bias in model building.

    Purpose of the Study:

    • To present a novel computer program for stepwise variable selection in logistic regression.
    • To implement a selection procedure based on maximum likelihood estimation and likelihood ratio tests.
    • To demonstrate the program's utility through practical examples.

    Main Methods:

    • The program employs a stepwise selection algorithm for logistic regression.
    • Variable selection is guided by likelihood ratio tests on regression coefficients.

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  • Both forward selection and backward elimination strategies are incorporated at each step.

    • Main Results:

    • The developed computer program effectively performs stepwise variable selection for logistic regression.
    • The methodology, utilizing likelihood ratio tests, provides a robust approach to model building.
    • Illustrative examples confirm the program's practical applicability and performance.

    Conclusions:

    • The computer program offers an efficient tool for logistic regression model development.
    • Stepwise selection based on likelihood ratio tests is a valuable technique for identifying significant predictors.
    • The program facilitates the creation of interpretable and statistically sound logistic regression models.