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Malaysian Journal of Medical Sciences
School of Medical Sciences, Universiti Sains Malaysia
ISSN: 1394-195X
Vol. 25, No. 4, 2018, pp. 122-130
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Bioline Code: mj18053
Full paper language: English
Document type: Research Article
Document available free of charge
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Malaysian Journal of Medical Sciences, Vol. 25, No. 4, 2018, pp. 122-130
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Sample Size Guidelines for Logistic Regression from Observational Studies with Large Population: Emphasis on the Accuracy Between Statistics and Parameters Based on Real Life Clinical Data
Bujang, Mohamad Adam; Sa’at, Nadiah; Tg Abu Bakar Sidik, Tg Mohd Ikhwan & Lim, Chien Joo
Abstract
Background: Different study designs and population size may require different sample
size for logistic regression. This study aims to propose sample size guidelines for logistic
regression based on observational studies with large population.
Methods: We estimated the minimum sample size required based on evaluation from
real clinical data to evaluate the accuracy between statistics derived and the actual parameters.
Nagelkerke r-squared and coefficients derived were compared with their respective parameters.
Results: With a minimum sample size of 500, results showed that the differences between
the sample estimates and the population was sufficiently small. Based on an audit from a medium
size of population, the differences were within ± 0.5 for coefficients and ± 0.02 for Nagelkerke
r-squared. Meanwhile for large population, the differences are within ± 1.0 for coefficients and
± 0.02 for Nagelkerke r-squared.
Conclusions: For observational studies with large population size that involve logistic
regression in the analysis, taking a minimum sample size of 500 is necessary to derive the statistics
that represent the parameters. The other recommended rules of thumb are EPV of 50 and formula;
n = 100 + 50i where i refers to number of independent variables in the final model.
Keywords
logistic regression; observational studies; sample size
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