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Related Experiment Videos

Selecting an exposure lag period

A Salvan1, L Stayner, K Steenland

  • 1National Institute for Occupational Safety and Health, Cincinnati, OH 45226, USA.

Epidemiology (Cambridge, Mass.)
|July 1, 1995
PubMed
Summary
This summary is machine-generated.

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Epidemiologists often favor larger exposure effect estimates, but this study shows inconsistencies. A goodness-of-fit measure is a more reliable criterion for selecting exposure-lag values than the highest relative risk estimate.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical modeling

Background:

  • Epidemiological studies often prioritize larger exposure effect estimates (e.g., relative risks) when selecting exposure-lag parameters.
  • This practice assumes higher estimates indicate greater credibility, potentially influencing the choice of exposure-lag values in epidemiological research.

Purpose of the Study:

  • To evaluate the validity of using the highest exposure effect estimate as a criterion for selecting exposure-lag parameters.
  • To compare the highest-estimate criterion with a goodness-of-fit measure for estimating exposure-weighting parameters in epidemiological models.

Main Methods:

  • Utilized hypothetical data to estimate exposure-lag parameters through trial-and-error fitting.
  • Compared the behavior of the likelihood-ratio goodness-of-fit statistic with the relative risk across different parameter values.

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  • Assessed inconsistencies between the highest-estimate and likelihood-based goodness-of-fit criteria.

    • Main Results:

    • Demonstrated potential inconsistencies between selecting exposure-lag parameters based on the highest relative risk estimate versus goodness-of-fit.
    • The likelihood-ratio goodness-of-fit statistic provides an alternative, potentially more robust, criterion for parameter estimation.
    • Observed discrepancies highlight the limitations of relying solely on the magnitude of effect estimates.

    Conclusions:

    • The highest-estimate criterion for selecting exposure-lag parameters in epidemiological studies is potentially flawed and should be avoided.
    • Recommended using a priori biological knowledge or goodness-of-fit criteria for estimating exposure-weighting parameters.
    • Emphasized the importance of robust statistical methods over simple magnitude-based selection for parameter estimation.