Contents
Why do we need a minimum sample size for logistic regression?
Typically, we want to determine a minimum sample size so as to achieve a minimally acceptable level of statistical power.
When to use logistic regression in an observational study?
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). Since the purpose of most of statistical analyses is for inference, determination of sample size requirement is necessary before the analysis is conducted.
How big is the sample size for penalized regression?
I’m performing a repeated analysis on subgroups of a population (penalized regression). Some of this subgroups have a moderate-large sample size (103 individuals). Some others have a total number of observations of 1, 4, 5 ,7 ,9 , 19 and so on.
How many independent variables should be included in logistic regression?
I have seven independent variables (three continuous and four nominal). One guideline suggest that there should be 10 cases for each predictor / independent variable (Agresti, 2007). Based on this guideline I feel that it is OK to run logistic regression. Am I right? If not please let me know how to decide the number of independent variables?
What are the methods of logistic regression analysis?
This paper describes the problem of logistic regression analysis with the sample size determination algorithms, namely the methods of univariate statistics, logistics regression, cross-validation and Bayesian inference.
Is there a problem with logistic regression for rare events?
Although King and Zeng accurately described the problem and proposed an appropriate solution, there are still a lot of misconceptions about this issue. The problem is not specifically the rarity of events, but rather the possibility of a small number of cases on the rarer of the two outcomes.