Pitfalls in prediction modeling research

This paper discusses 11 pitfalls in the design, analysis, and reporting of medical prediction modeling research. These concern pitfalls related to timing and quality of measurements (ie, incorporating future predictors, differences in the precision of measurements between development and implementation, outcome misclassification, and predictors with missing values), pitfalls related to modeling (ie, univariate pre-selection of potential predictors, overfitting the data, and simplifying the model too much), pitfalls related to reporting and model interpretation (ie, being unclear about the prediction time-horizon, ignoring treatments after time-zero, and conflating prediction and causal research), and pitfalls related to the focus on something new (ie, developing a new model).