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July 30, 1998Statistics in Medicine1,143 citations

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

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FHF. HsiehDBD. BlöchMLMichael Larsen

Key Points

  • To present and validate a simplified method for calculating sample sizes in simple and multiple linear and logistic regression models.
  • Adapted standard sample size formulas used for comparing means or proportions to simple linear and logistic regression models.
  • Adjusted sample size requirements for multiple covariates using a variance inflation factor without assuming low event probability.
  • Evaluated the accuracy of the proposed approach and existing sample-size software tools against computer power simulations.
  • The formula successfully determines sample sizes for multiple linear and logistic regression models without requiring restrictive low response probability assumptions.
  • Power simulation comparisons verified that the simplified calculations provide accuracy comparable to existing specialized statistical software.

Abstract

A sample size calculation for logistic regression involves complicated formulae. This paper suggests use of sample size formulae for comparing means or for comparing proportions in order to calculate the required sample size for a simple logistic regression model. One can then adjust the required sample size for a multiple logistic regression model by a variance inflation factor. This method requires no assumption of low response probability in the logistic model as in a previous publication. One can similarly calculate the sample size for linear regression models. This paper also compares the accuracy of some existing sample-size software for logistic regression with computer power simulations. An example illustrates the methods.

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Cite This Study

Hsieh et al. (1998) studied this question.

synapsesocial.com/papers/69d77120086f9d6299f310a8https://doi.org/10.1002/(sici)1097-0258(19980730)17:14<1623::aid-sim871>3.0.co;2-s
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