Bring More Data!-A Good Advice? Removing Separation in Logistic Regression by Increasing Sample Size.

ABSTRACT: The parameters of logistic regression models are usually obtained by the method of maximum likelihood (ML). However, in analyses of small data sets or data sets with unbalanced outcomes or exposures, ML parameter estimates may not exist. This situation has been termed 'separation' as the two outcome groups are separated by the values of a covariate or a linear combination of covariates. To overcome the problem of non-existing ML parameter estimates, applying Firth's correction (FC) was proposed. In practice, however, a principal investigator might be advised to 'bring more data' in order to solve a separation issue. We illustrate the problem by means of examples from colorectal cancer screening and ornithology. It is unclear if such an increasing sample size (ISS) strategy that keeps sampl

SUBMITTER: Sinkovec H 

PROVIDER: S-EPMC6926877 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature