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Analysis and Specification of Starch Granule Size Distributions
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Sample size calculations for skewed distributions.

Bonnie Cundill1, Neal D E Alexander2

  • 1MRC Tropical Epidemiology Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK. bonnie.cundill@lshtm.ac.uk.

BMC Medical Research Methodology
|April 19, 2015
PubMed
Summary
This summary is machine-generated.

Sample size calculations for skewed data using generalized linear models (GLMs) are more accurate than normal approximations. This method, particularly for negative binomial and gamma distributions, improves statistical power in study designs.

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Area of Science:

  • Biostatistics
  • Statistical modeling

Background:

  • Sample size calculations are crucial for study validity.
  • Normal approximations are often used for non-normal data, even with generalized linear models (GLMs).

Purpose of the Study:

  • To derive sample size formulas using GLM theory for comparing two means.
  • To evaluate the performance of normal approximations against GLM-based calculations.

Main Methods:

  • Developed sample size formulas based on GLM theory for various distributions (negative binomial, Poisson, binomial, gamma).
  • Employed simulations to compare GLM-based calculations with normal approximations using identity and log link functions.

Main Results:

  • GLM-based calculations on the log scale performed well for negative binomial and gamma distributions, outperforming normal approximations.
  • The advantage of GLM-based calculations was less pronounced for Poisson and binomial distributions.

Conclusions:

  • The proposed GLM-based method is effective for sample size calculations when comparing means of highly skewed outcome variables.
  • This approach enhances the reliability of sample size determination in biostatistical analyses.