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Logistic regression methods for retrospective case-control studies using complex sampling procedures.

T R Fears, C C Brown

    Biometrics
    |December 1, 1986
    PubMed
    Summary
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    This study introduces a new method for case-control studies using stratified sampling. It provides maximum likelihood estimators for logistic models, improving analysis of disease risk factors.

    Area of Science:

    • Epidemiology
    • Biostatistics

    Background:

    • Case-control studies are essential for investigating disease risk factors.
    • Stratified sampling is commonly used to control for confounding variables in epidemiological research.
    • Existing methods for stratified case-control studies often require logistic models that include stratum-specific terms.

    Purpose of the Study:

    • To develop maximum likelihood estimators for logistic regression models in case-control studies with stratified samples.
    • To address situations where the logistic model does not include explicit terms for strata.
    • To estimate parameters for variables that are completely associated with strata.

    Main Methods:

    • The study considers case-control data from stratified samples.
    • It assumes a logistic model where disease probability is independent of stratum for fixed covariates.

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  • Maximum likelihood estimation is employed, utilizing knowledge of stratum proportions and total stratum sizes.

    • Main Results:

    • The proposed method yields maximum likelihood estimators for all logistic model parameters, including those associated with strata.
    • This approach is applicable even when the logistic model does not explicitly account for strata.
    • The methodology extends to estimators under general probability sampling.

    Conclusions:

    • The developed method provides robust parameter estimation in stratified case-control studies.
    • It offers a flexible alternative to traditional methods, particularly when stratum effects are not explicitly modeled.
    • This work enhances the analytical toolkit for epidemiological research involving complex sampling designs.