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A FORTRAN program for stratified analysis of data from case-control studies.

T D Koepsell

    Computers and Biomedical Research, an International Journal
    |April 1, 1983
    PubMed
    Summary

    This study introduces a FORTRAN program for epidemiologic case-control studies. It enables simultaneous covariate stratification and provides odds ratio estimates using the Mantel-Haenszel method.

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    Area of Science:

    • Epidemiology
    • Biostatistics
    • Computer Science

    Background:

    • Confounding and effect modification are critical challenges in epidemiologic case-control studies.
    • Stratification by covariates is a standard approach to address these issues.
    • Existing statistical packages may lack flexibility for simultaneous multi-covariate stratification.

    Purpose of the Study:

    • To describe a flexible FORTRAN program for simultaneous covariate stratification in case-control studies.
    • To provide tools for calculating summary odds ratios and detecting effect modification.
    • To offer data manipulation capabilities similar to larger statistical packages.

    Main Methods:

    • Development of a FORTRAN program for data analysis.
    • Implementation of the Mantel-Haenszel method for odds ratio estimation.
    • Inclusion of functionality for simultaneous stratification by multiple covariates.
    • Features for data modification and subject exclusion.

    Main Results:

    • The program facilitates simultaneous stratification by several covariates.
    • It produces summary odds ratio estimates and chi-square statistics via the Mantel-Haenszel method.
    • Individual covariate effect modification detection is supported.
    • User-friendly data input and modification options are available.

    Conclusions:

    • The described FORTRAN program offers a flexible and efficient solution for complex case-control study analyses.
    • It effectively addresses confounding and effect modification through simultaneous covariate stratification.
    • The program's capabilities enhance the analysis of epidemiologic data, particularly in handling multiple covariates.

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