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Related Experiment Videos

Sampling strategies for occupational exposure assessment under generalized linear model.

Chu-Chih Chen1, Cheng-Lin Chuang, Kuen-Yuh Wu

  • 1Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan Town, Miaoli County 350, Taiwan. ccchen@nhri.org.tw

The Annals of Occupational Hygiene
|May 23, 2009
PubMed
Summary

Determining sample size for occupational exposure studies is crucial. For comparing worker groups, more workers with fewer measurements are economical. For long-term trends, more measurements reduce the needed sample size.

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Area of Science:

  • Industrial Hygiene
  • Occupational Health
  • Environmental Science

Background:

  • Occupational exposure assessment is vital in industrial hygiene.
  • Study designs, particularly for sample size and repeated measurements, are under-discussed.
  • Statistical analyses of exposure magnitudes and variations are extensive, but design aspects are limited.

Purpose of the Study:

  • To propose a general framework for sampling strategies in occupational exposure studies.
  • To determine sample size requirements and the number of repeated measurements.
  • To provide a method for hypothesis testing on mean exposure differences and long-term trends.

Main Methods:

  • Utilized a mixed-effects generalized linear model (GLM) framework.
  • Developed sampling strategies for sample size and repeated measurements.
  • Applied log-normal distribution assumptions for illustrative examples.

Main Results:

  • Derived and tabulated explicit sample size requirements for two cases under log-normal distribution.
  • Sample size is more dominant than repeated measurements for group exposure comparisons.
  • Sample size for long-term trend testing decreases with more repeated measurements.

Conclusions:

  • For group comparisons, recruiting more workers with fewer measurements is often more economical.
  • For long-term trend analysis, increasing repeated measurements significantly reduces sample size needs.
  • Optimal sampling for trend analysis involves equally spaced times to cancel between-worker variance effects.