Effects of influential points and sample size on the selection and replicability of multivariable fractional polynomial models.

ABSTRACT:

Background

The multivariable fractional polynomial (MFP) approach combines variable selection using backward elimination with a function selection procedure (FSP) for fractional polynomial (FP) functions. It is a relatively simple approach which can be easily understood without advanced training in statistical modeling. For continuous variables, a closed test procedure is used to decide between no effect, linear, FP1, or FP2 functions. Influential points (IPs) and small sample sizes can both have a strong impact on a selected function and MFP model.

Methods

We used simulated data with six continuous and four categorical predictors to illustrate approaches which can help to identify IPs with an influence on function selection and the MFP model. Approaches use leave-one or two

SUBMITTER: Sauerbrei W 

PROVIDER: S-EPMC10111698 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature