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According to the paper, adjustment needed to be made for the sample size tables such as dividing the estimated sample size with a factor of (1– p 2) when sample size need to be estimated for logistic regression. Earlier on, Hsieh ( 5) proposed a sample size table for logistic regression but limited the estimation for only one covariate. The sample size requirement for logistic regression has been discussed in the literature. Since the purpose of most of statistical analyses is for inference, determination of sample size requirement is necessary before the analysis is conducted. In observational studies, logistic regression is commonly used to determine the associated factors with or without controlling for specific variables and also for predictive modelling ( 1– 4). survived versus died or poor outcome versus good outcome), logistic regression also requires less assumptions as compared to multiple linear regression or Analysis of Covariance (ANCOVA). Beside the fact that most clinical outcomes are defined as binary form (e.g.
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Logistic regression is one of the most utilised statistical analyses in multivariable models especially in medical research.