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Only the intercept of the original model (derivation study) is adjusted.
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They allow subjecting each study's data to formal meta-analysis to produce pooled estimates not only of discrimination, but also of calibration by applying previously published models (derivation study) to a new, validation population.
Studies could develop new prognostic models (derivation studies) or evaluate previous ones (validation studies).
As confirmation, a logistic regression model (with derivation study coefficients) was applied to CPR-based dummy variables in the validation study.
Fitting the stratified model (equation 1) is a 1-stage approach to model derivation across studies.
In the original model derivation and validation study, a strong association was observed between the predicted SN or FN risk during chemotherapy cycle 1 and the actual FN risk in cycles 1 4 (Fig. S1).
9, 10 The proper validation implies that a fully specified, originally existing model (both the derivation study variables and coefficients) is used to compute the probability and obtain predicted outcomes and then compare the estimates to the actual outcomes in the validation study.
The equation of the prediction model in the derivation study of the ABCD rule is: Log (risk of stroke 1 − risk of stroke ) = linear predictor Y = α + β INT X INT + β HIGH X HIGH (1 where α is the intercept and βINT and βHIGH are the regression coefficients of the dummy variables "intermediate risk" and "high risk", respectively.
The performance of these models from derivation studies ranged from an AUROC of 0.75 for AFSS to 0.88 for the PIRO score.
Unfortunately, the majority of published prediction research focuses solely on model derivation, and validation studies are scarce.
As a second objective, we aimed at confirming the functionality of the approach by using LR model coefficients from the derivation study to the validation study data.
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