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Related Experiment Videos

A simple method of sample size calculation for linear and logistic regression

F Y Hsieh1, D A Bloch, M D Larsen

  • 1CSPCC, Department of Veterans Affairs, Palo Alto Health Care System (151-K), California 94304, USA.

Statistics in Medicine
|August 12, 1998
PubMed
Summary

Calculating sample size for logistic regression is simplified using formulae for comparing means or proportions. This approach offers a practical method for determining sample size requirements in regression analysis.

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Area of Science:

  • Biostatistics
  • Statistical Modeling

Background:

  • Sample size calculation for logistic regression typically involves complex formulas.
  • Existing methods may rely on assumptions like low response probability, limiting their applicability.

Purpose of the Study:

  • To propose simplified methods for sample size calculation in logistic regression.
  • To provide a practical approach for determining sample sizes for both simple and multiple logistic regression models.
  • To evaluate the accuracy of existing sample-size software for logistic regression.

Main Methods:

  • Utilizing sample size formulas for comparing means or proportions for simple logistic regression.
  • Adjusting sample size for multiple logistic regression using a variance inflation factor.
  • Conducting computer power simulations to compare the accuracy of existing sample-size software.

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Main Results:

  • The proposed method simplifies sample size calculation for logistic regression without assuming low response probability.
  • The variance inflation factor provides an adjustment for multiple logistic regression models.
  • Simulations offer insights into the performance of current sample-size software.

Conclusions:

  • Simplified sample size calculations are feasible for logistic regression using existing statistical formulas.
  • The proposed methods offer a practical alternative for researchers in biostatistics and related fields.
  • Accurate sample size determination is crucial for the validity of regression models.