Analyses of 'change scores' do not estimate causal effects in observational data.

ABSTRACT:

Background

In longitudinal data, it is common to create 'change scores' by subtracting measurements taken at baseline from those taken at follow-up, and then to analyse the resulting 'change' as the outcome variable. In observational data, this approach can produce misleading causal-effect estimates. The present article uses directed acyclic graphs (DAGs) and simple simulations to provide an accessible explanation for why change scores do not estimate causal effects in observational data.

Methods

Data were simulated to match three general scenarios in which the outcome variable at baseline was a (i) 'competing exposure' (i.e. a cause of the outcome that is neither caused by nor causes the exposure), (ii) confounder or (iii) mediator for the total causal effect of the exposur

SUBMITTER: Tennant PWG 

PROVIDER: S-EPMC9557845 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature