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Related Experiment Videos

Applications of microcomputer spreadsheet packages as adjuncts to multiple logistic regression analysis.

S Lemeshow, H Pastides, J S Avrunin

    American Journal of Epidemiology
    |October 1, 1985
    PubMed
    Summary

    Epidemiologists can use microcomputer spreadsheets for simplified computation of multiple logistic regression (MLR) probabilities and odds ratios. This approach reduces the need for complex calculations and reliance on mainframe computers.

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    Area of Science:

    • Epidemiology
    • Biostatistics
    • Health Informatics

    Background:

    • Epidemiologists often face complex calculations for statistical models.
    • Traditional methods for computing multiple logistic regression (MLR) can be time-consuming and require specialized expertise.
    • Increasing accessibility of microcomputers in research settings presents opportunities for streamlined data analysis.

    Purpose of the Study:

    • To demonstrate the utility of microcomputer spreadsheet software for epidemiologists.
    • To simplify the computation of multiple logistic regression (MLR) probabilities, odds ratios, and confidence intervals.
    • To reduce the reliance on mainframe computers and manual calculations in epidemiological research.

    Main Methods:

    • Utilizing a microcomputer spreadsheet package for data entry and computation.

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  • Formatting spreadsheets to automate the calculation of MLR-derived statistics.
  • Implementing a user-friendly system requiring no direct arithmetic manipulation by the user.

    • Main Results:

    • Spreadsheet formatting significantly simplifies data entry for MLR analysis.
    • Automated computations eliminate the need for manual arithmetic, reducing potential errors.
    • Clerical support staff can assist in complex statistical computations, enhancing efficiency.

    Conclusions:

    • Microcomputer spreadsheet packages offer a practical and efficient tool for epidemiological statistical analysis.
    • This method enhances accessibility to complex statistical computations, empowering a wider range of research staff.
    • The adoption of spreadsheet-based analysis decreases dependence on traditional computing resources in clinical and research environments.