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Related Experiment Videos

Sample size and power based on the population attributable fraction.

W S Browner1, T B Newman

  • 1Department of Medicine, Veterans Administration Medical Center, San Francisco 94121.

American Journal of Public Health
|September 1, 1989
PubMed
Summary
This summary is machine-generated.

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Calculating sample size for disease association studies requires careful consideration of the measure used. Population attributable fraction (PAF) is often more relevant for public health than relative risk (RR), impacting study power and sample size calculations.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Sample size calculations commonly rely on relative risk (RR) to assess exposure-disease associations.
  • Population attributable fraction (PAF) offers a more direct measure of public health impact.

Purpose of the Study:

  • To determine sample size and power for detecting a specified PAF in cohort and case-control studies.
  • To compare these calculations with traditional RR-based methods.

Main Methods:

  • Developed sample size and power calculations for PAF detection.
  • Compared findings with RR-based sample size and power calculations.
  • Evaluated performance in cohort and case-control study designs.

Main Results:

Related Experiment Videos

  • For rare exposures, adequate power to detect a small PAF may be achieved even with low power to detect a small RR.
  • For common exposures, large studies might lack sufficient power to detect a small PAF.

Conclusions:

  • The choice between RR and PAF for sample size and power calculations depends on the exposure's prevalence and the research question.
  • Selecting the most pertinent measure (RR or PAF) is crucial for accurate study design and interpretation.