Modelling count, bounded and skewed continuous outcomes in physical activity research: beyond linear regression models.

ABSTRACT:

Background

Inference using standard linear regression models (LMs) relies on assumptions that are rarely satisfied in practice. Substantial departures, if not addressed, have serious impacts on any inference and conclusions; potentially rendering them invalid and misleading. Count, bounded and skewed outcomes, common in physical activity research, can substantially violate LM assumptions. A common approach to handle these is to transform the outcome and apply a LM. However, a transformation may not suffice.

Methods

In this paper, we introduce the generalized linear model (GLM), a generalization of the LM, as an approach for the appropriate modelling of count and non-normally distributed (i.e., bounded and skewed) outcomes. Using data from a study of physical activity among o

SUBMITTER: Akram M 

PROVIDER: S-EPMC10163772 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature