Analyses of 'change scores' do not estimate causal effects in observational data. (7th June 2021)

• Record Type: Journal Article
• Authors: Tennant, Peter W G; Arnold, Kellyn F; Ellison, George T H; Gilthorpe, Mark S
• Is Part Of: International journal of epidemiology. Volume 51:Number 5(2022)
• Page Start: 1604
• Page End: 1615
• Publication Date: 2021-06-07
• Subjects: Analysis of change -- change scores -- difference scores -- gain scores -- change-from-baseline variables -- directed acyclic graphs; Epidemiology -- Periodicals; 614.4
• DOI: 10.1093/ije/dyab050
• Languages: English
• ISSNs: 0300-5771

Abstract

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 exposure variable at baseline on the outcome variable at follow-up. Regression coefficients were compared between change-score analyses and the appropriate estimator(s) for the total and/or direct causal effect(s). Results: Change-score analyses do not provide meaningful causal-effect estimates unless the baseline outcome variable is a 'competing exposure' for the effect of the exposure on the outcome at follow-up. Where the baseline outcome is a confounder or mediator, change-score analyses evaluate obscure estimands, which may diverge substantially in magnitude and direction from the total and direct causal effects. Conclusion: Future observational studies that seek causal-effect estimates should avoid analysing change scores and adopt alternative analytical strategies.