Current recommendations (PROGRESS recommendations in parentheses) |
Register study and publish a protocol (Recommendation 10) Recruit a representative and well-defined sample at a common, early time point (Recommendation 14) Ensure complete follow-up of sufficient length Choose prognostic factors measured based on sound theory Blind outcome assessors Account for covariates statistically Ensure sample size is large enough to assess multiple prognostic factors (10 outcome-events-per-predictor-rule) Validate the model Report all results explicitly and transparently (Recommendation 15)
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Select candidate predictors that are clinically relevant Evaluate data quality (Recommendation 20) Describe data handling decisions for example, continuous variables should be analysed on their continuous scale (Recommendation 13) Select variables to be included in the final model using a prespecified strategy Assess the performance of the model (internal validation), ie, overall performance, discrimination and calibration
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Prespecify acceptable performance of the model Assess overall performance, discrimination, and calibration in the validation sample Include a validation plot Update and recalibrate the model
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Adjust the tool for clinical use Use a simple interface Do not refer to the tool as a ‘rule’ Make sure all aspects of the tool are clear and unambiguous Include uncertainty interval (95%CI) around posterior probability estimates
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Take care with underpowered statistical analyses that are not pre-planned Report all subgroup findings Subgroup analyses should be replicated in new data Analyse continuous outcomes on their continuous scale Design RCTs to be 4 arm, ie, intervention and control in groups +ve and –ve on rule (Recommendation 22) Studies should compare ‘stratified’ vs ‘all-comer’ approaches (Recommendation 23)
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