Analyses of ‘change scores’ do not estimate causal effects in observational data
| Autor: | Peter W. G. Tennant, George T. H. Ellison, Mark S. Gilthorpe, Kellyn F Arnold |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Epidemiology Statistics - Applications Methodology (stat.ME) 03 medical and health sciences 0302 clinical medicine Statistics Linear regression Humans Applications (stat.AP) 030212 general & internal medicine Baseline (configuration management) skin and connective tissue diseases Statistics - Methodology 030503 health policy & services Causal effect Confounding Estimator Confounding Factors Epidemiologic General Medicine Outcome (probability) Causality Variable (computer science) Observational study sense organs 0305 other medical science Psychology I260 |
| ISSN: | 0300-5771 |
| Popis: | 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 estimate causal effects in observational data. Methods: Data were simulated to match three general scenarios where the variable representing measurements of the outcome at baseline was a 1) competing exposure, 2) confounder, or 3) mediator for the total causal effect of the exposure on the variable representing measurements of the outcome at follow-up. Regression coefficients were compared between change-score analyses and DAG-informed analyses. Results: Change-score analyses do not provide meaningful causal effect estimates unless the variable representing measurements of the outcome at baseline is a competing exposure, as in a randomised experiment. Where such variables (i.e. baseline measurements of the outcome) are confounders or mediators, the conclusions drawn from analyses of change scores diverge (potentially substantially) from those of DAG-informed analyses. Conclusions: Future observational studies that seek causal effect estimates should avoid analysing change scores and adopt alternative analytical strategies. Comment: 15 pages, 3 figures |
| Databáze: | OpenAIRE |
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