Prediction internal validation
Estimate optimism using appropriate resampling
Repeat imputation, transformations, feature selection and tuning in the appropriate resampling loops. Respect repeated patients, hospitals and chronology. Internal validation estimates optimism; it does not demonstrate performance in a new setting.
Flow diagram
Steps
Choose resampling design
Justify bootstrap, cross-validation or other internal validation for the data structure.
Respect clustering and chronology
Keep related records together and preserve temporal separation when needed.
Repeat the full modeling pipeline
Repeat imputation, transformations, predictor selection and tuning within appropriate training or nested resampling loops. Evaluate predictions without reusing their outcomes for fitting decisions.
Estimate optimism and uncertainty
Define performance measures, correction and interval methods.
Report validation limitations
Distinguish internally validated performance from external transportability.
Prediction internal validation
End flow
Tables and supporting information
Resampling specification
Specify repetitions and seeds as study inputs; avoid fixed defaults.
Assumptions and rationale
Internal validation does not establish performance in new institutions, populations or later periods.
Method and protocol evidence
Attach the protocol, methodological sources and versioned software specification supporting these decisions. Record citations and provenance; do not enter participant identifiers.