Prediction versus aetiology: common pitfalls and how to avoid them.
Prediction research is a distinct field of epidemiologic research, which should be clearly separated from aetiological research. Both prediction and aetiology make use of multivariable modelling, but the underlying research aim and interpretation of results are very different. Aetiology aims at uncovering the causal effect of a specific risk factor on an outcome, adjusting for confounding factors that are selected based on pre-existing knowledge of causal relations. In contrast, prediction aims at accurately predicting the risk of an outcome using multiple predictors collectively, where the final prediction model is usually based on statistically significant, but not necessarily causal, associations in the data at hand.In both scientific and clinical practice, however, the two are often confused, resulting in poor-quality publications with limited interpretability and applicability. A major problem is the frequently encountered aetiological interpretation of prediction results, where individual variables in a prediction model are attributed causal meaning. This article stresses the differences in use and interpretation of aetiological and prediction studies, and gives examples of common pitfalls.
Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
Merel
M
van Diepen
Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
Chava L
CL
Ramspek
ERA-EDTA Registry, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
Kitty J
KJ
Jager
CNR-IBIM Clinical Epidemiology and Pathophysiology of Renal Diseases and Hypertension, Reggio Calabria, Italy.
Carmine
C
Zoccali
Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
Friedo W
FW
Dekker
D015984:Causality; D016021:Epidemiologic Studies; D006801:Humans; D008962:Models, Theoretical; D011379:Prognosis; D012107:Research Design; D012307:Risk Factors
aetiological research; causality; multivariable modelling; prediction research; risk prediction