Chance Owing to chance, the direction of the predictor–outcome relation can be unexpected, especially when samples are small. |
Advanced predictor-selection strategies Proper design of prediction-model studies with suitable sample size calculation Delete predictor from the model
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Misclassification Unexpected findings owing to misclassification can occur when a predictor is measured or coded with error, the predictor– outcome relation is modelled incorrectly or 2 or more variables are included even though they are collinear. |
Redo the measurement/reclassify Delete erroneous value (if known) and impute Model predictor on its continuous scale and consider nonlinear trends Include strongest predictor of 2 collinear variables Combine 2 collinear variables into 1 predictor Delete predictor from the model
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Selection An unexpected finding occurs when selection is related to both the predictor and the outcome, either at inclusion, during follow-up or during the outcome assessment (Figure 1). |
Apply weighting to undo selection process Add participants to undo the selection process Clearly define the domain in which the model is applicable Delete predictor from the model
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Mixing of effects (confounding) When 2 causes of the outcome are mutually related, the observed effect of one can be mixed up with the effect of the other, potentially resulting in an unexpected finding (Figure 1). |
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Intervention effects A predictor value can trigger a medical intervention, which subsequently lowers the probability of the outcome, thereby attenuating the observed relation between the predictor and the outcome (Figure 1). |
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Heterogeneity Predictor effects may differ across subgroups (i.e., interaction or heterogeneity of predictor effects). If the distribution of the subgrouping factor in the study population differs from the distribution of this factor in the typical patient population, this may lead to an unexpected finding. |
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