Detecting potential outliers in longitudinal data with time-dependent covariates.

ABSTRACT:

Background

Outliers can influence regression model parameters and change the direction of the estimated effect, over-estimating or under-estimating the strength of the association between a response variable and an exposure of interest. Identifying visit-level outliers from longitudinal data with continuous time-dependent covariates is important when the distribution of such variable is highly skewed.

Objectives

The primary objective was to identify potential outliers at follow-up visits using interquartile range (IQR) statistic and assess their influence on estimated Cox regression parameters.

Methods

Study was motivated by a large TEDDY dietary longitudinal and time-to-event data with a continuous time-varying vitamin B₁₂ intake as the exposure of interes

SUBMITTER: Mramba LK 

PROVIDER: S-EPMC11003829 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature