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The "Dry-Run" Analysis: A Method for Evaluating Risk Scores for Confounding Control.

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    Evaluating disease risk score (DRS) models for confounding control is challenging. A novel "dry-run" analysis, validated through simulations and an empirical example, offers a promising method for assessing DRS model performance in controlling confounding.

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    Area of Science:

    • Epidemiology
    • Biostatistics
    • Health Services Research

    Background:

    • Assessing propensity score (PS) models for confounding control is standard via covariate balance checks.
    • Evaluating disease risk score (DRS) models for confounding control lacks clear, established methods.
    • Traditional DRS evaluation relies on prediction diagnostics and goodness-of-fit tests, not direct confounding control assessment.

    Purpose of the Study:

    • To introduce and evaluate the "dry-run" analysis as a method for assessing confounding control in DRS models.
    • To compare the dry-run analysis with traditional DRS performance metrics using simulations and an empirical dataset.
    • To determine if the dry-run analysis improves the assessment of confounding control compared to existing methods.

    Main Methods:

    • Simulations were conducted to compare the dry-run analysis with standard metrics like the C statistic and goodness-of-fit tests.
    • An empirical dataset was used to compare propensity score (PS) matching and DRS matching.
    • The dry-run analysis involves creating "pseudo-exposed" and "pseudo-unexposed" groups within the unexposed population to mimic real exposure group covariate differences.

    Main Results:

    • In simulations, the dry-run analysis frequently enhanced the assessment of confounding control compared to the C statistic and goodness-of-fit tests.
    • The empirical example showed similar results between PS and DRS matching.
    • PS matching demonstrated good covariate balance, while DRS matching performed well in controlling confounding during the dry-run analysis.

    Conclusions:

    • The dry-run analysis shows potential as a valuable tool for evaluating the confounding control capabilities of disease risk score models.
    • This method offers an alternative approach to traditional diagnostics for assessing the validity of DRS models in observational studies.
    • Further application of the dry-run analysis is recommended for robust confounding control assessment in epidemiological research.