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

Published: Jul 5, 2019
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 of why change scores do not...
Paper Details
Title
Analyses of 'change scores' do not estimate causal effects in observational data
Published Date
Jul 5, 2019
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