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Selecting risk factors: a comparison of discriminant analysis, logistic regression and Cox's regression model using

T Brenn, E Arnesen

    Statistics in Medicine
    |October 1, 1985
    PubMed

    Insights

    Discriminant analysis is an efficient method for identifying risk factors for coronary heart disease mortality. It offers comparable results to logistic regression and Cox

    Area of Science:

    • Epidemiology
    • Biostatistics
    • Cardiovascular Disease Research

    Background:

    • Identifying risk factors for mortality is crucial in cardiovascular disease research.
    • Various statistical models exist for risk factor selection, each with computational and analytical considerations.

    Purpose of the Study:

    • To comparatively evaluate discriminant analysis, logistic regression, and Cox's model for selecting risk factors of total and coronary deaths.
    • To assess the efficiency and accuracy of these statistical methods in a large cohort study.

    Main Methods:

    • Comparative analysis of discriminant analysis, logistic regression, and Cox's model.
    • Application of methods to a cohort of 6595 men aged 20-49 followed for 9 years.
    • Evaluation of variable set selection, computational time, and coefficient estimation.

    Main Results:

    • Discriminant analysis, logistic regression, and Cox's model identified similar sets of risk factors.
    • Discriminant analysis was significantly faster computationally, especially for large datasets.
    • Non-stepwise analyses yielded nearly identical coefficient estimates across all methods.

    Conclusions:

    • Discriminant analysis is recommended for preliminary or stepwise risk factor selection due to its efficiency.
    • Cox's model is suggested for non-stepwise analyses when computational time is less of a concern.
    • The choice of method can impact computational efficiency without significantly compromising the identification of key risk factors.

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