1. The peril of adjusting for baseline when using change as a predictor
- Author
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Kimmo Sorjonen, Ingre M, Bo Melin, and Daniel Falkstedt
- Subjects
PsyArXiv|Social and Behavioral Sciences ,Text mining ,bepress|Life Sciences ,business.industry ,PsyArXiv|Life Sciences ,bepress|Social and Behavioral Sciences ,Medicine ,PsyArXiv|Social and Behavioral Sciences|Quantitative Methods|Statistical Methods ,bepress|Social and Behavioral Sciences|Psychology|Quantitative Psychology ,PsyArXiv|Social and Behavioral Sciences|Quantitative Methods ,business ,Baseline (configuration management) ,Demography - Abstract
Some studies have analyzed the effect of a predictor measured at a later time point (X1), or of the X1-X0 difference, while adjusting for the predictor measured at baseline (X0), on some outcome Y of interest. The present simulation study shows that, if used to analyze the effect of change in X on Y, there is a high risk for this analysis to produce type 1-errors, especially with a strong correlation between true X and Y, when X0 and X1 are not measured with very high reliability, and with a large sample size. These problems are not encountered if analyzing the unadjusted effect of the X1-X0 difference on Y instead, and as this effect exhibits power on par with the adjusted effect it seems as the preferable method when using change between two measurement points as a predictor.
- Published
- 2019