Analyses of ‘change scores’ do not estimate causal effects in observational data | Zendy

P. W. G. Tennant | Zendy; Kellyn F Arnold | Zendy; George T. H. Ellison | Zendy; Mark S. Gilthorpe | Zendy

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

Author(s) -

P. W. G. Tennant,

Kellyn F Arnold,

George T. H. Ellison,

Mark S. Gilthorpe

Publication year - 2021

Publication title -

international journal of epidemiology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.406

H-Index - 208

eISSN - 1464-3685

pISSN - 0300-5771

DOI - 10.1093/ije/dyab050

Subject(s) - observational study , confounding , causal inference , outcome (game theory) , baseline (sea) , econometrics , marginal structural model , estimator , statistics , causal model , variable (mathematics) , regression , medicine , mathematics , mathematical analysis , oceanography , mathematical economics , geology

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.

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