Consequences of ignoring clustering in linear regression.

ABSTRACT:

Background

Clustering of observations is a common phenomenon in epidemiological and clinical research. Previous studies have highlighted the importance of using multilevel analysis to account for such clustering, but in practice, methods ignoring clustering are often employed. We used simulated data to explore the circumstances in which failure to account for clustering in linear regression could lead to importantly erroneous conclusions.

Methods

We simulated data following the random-intercept model specification under different scenarios of clustering of a continuous outcome and a single continuous or binary explanatory variable. We fitted random-intercept (RI) and ordinary least squares (OLS) models and compared effect estimates with the "true" value that had been used in

SUBMITTER: Ntani G 

PROVIDER: S-EPMC8265092 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature