calculating gpower for a logistic regression

The rules of thumb with 500 subjects were also been recommended by other studies ( 11– 12). Nemes and colleagues from their simulation study, showed that large sample size preferably 500 will increase the accuracy of the estimates ( 10). Besides that, studies with small to moderate samples size such as less than 100 usually overestimate the effect measure. The concept of EPV with 10 received some critics ( 8) and hence, Austin and Steyerberg recommended EPV of 20 instead ( 9). and Peduzzi et al., the concept of EPV of 10 is acceptable for both logistic regression and cox regression ( 6– 7).īased on EPV, researchers need to estimate the proportion for the outcome in the least category and divide it by 10 in order to determine the number of independent variables which can be studied. Another famous sample size guideline proposed that the minimum required sample size should be based on the rule of event per variable (EPV) ( 6). 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. Logistic regression is one of the most utilised statistical analyses in multivariable models especially in medical research.

Calculating gpower for a logistic regression