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A statistical method for assessing a threshold in epidemiological studies.

K Ulm1

  • 1Institute for Medical Statistics and Epidemiology, Technical University Munich, Federal Republic of Germany.

Statistics in Medicine
|March 1, 1991
PubMed
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This study introduces a new method for identifying threshold effects in epidemiological research, crucial for occupational health standards like threshold limit values (TLVs). The approach uses logistic regression and likelihood-ratio tests to accurately estimate and test for these critical exposure thresholds.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Occupational Medicine

Background:

  • Threshold effects, where risk is present only above a certain exposure level, are common in occupational health.
  • Established occupational exposure limits, such as threshold limit values (TLVs), often assume such thresholds.
  • Current statistical models typically do not account for threshold effects, potentially leading to inaccurate risk assessments.

Purpose of the Study:

  • To develop and validate a statistical method for estimating and testing threshold effects in epidemiological studies.
  • To provide a robust framework for analyzing exposure-response relationships where a critical exposure level exists.

Main Methods:

  • The proposed method utilizes logistic regression for modeling dichotomous outcomes.

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  • Estimation is performed using maximum-likelihood techniques.
  • A likelihood-ratio statistic (R) is employed for hypothesis testing, with its distribution approximated by a quasi one-sided chi-squared distribution.

    • Main Results:

    • The method successfully estimates and tests for threshold effects within the logistic regression framework.
    • Simulation studies support the validity of the statistical test under the null hypothesis of no threshold.
    • Application to occupational dust exposure data demonstrated the method's utility in identifying a threshold for chronic bronchitic reactions.

    Conclusions:

    • The developed method offers a statistically sound approach to identifying and evaluating threshold effects in epidemiological data.
    • This has direct implications for setting evidence-based occupational exposure limits and understanding environmental health risks.
    • The findings were further validated using bootstrap resampling, enhancing confidence in the results.