It has been suggested that clinical prediction rules are not reproducible, and that the most important variables frequently do not appear in replicate models. The authors studied the reproducibility of a validated rule for predicting radiographic evidence of pneumonia (ROC areas for the training and validation cohorts, 0.816 and 0.821, respectively). Two hundred replicate samples of size 250 and size 500 were generated by sampling without replacement from the original training cohort of 905 patients with a 14.6% prevalence of pneumonia. Forward selection was performed among 31 candidate variables by stepwise logistic regression. Using as reproducibility criteria: 1) inclusion of all five variables from the original model in the original order; 2) inclusion of all five variables in any order; 3) inclusion of the first three variables; 4) inclusion of the first two variables; 5) inclusion of the first variable; and 6) inclusion of any of the five variables: 2.5%, 13.5%, 48.5%, 85.5%, 98.0%, and 100% of replicate models of sample size 500, respectively, met the criteria, whereas 0%, 0%, 16.5%, 49.0%, 71.5%, and 97.5% of models of sample size 250 met the criteria (all comparisons by sample size p < .0001 except for criteria 1 and 6, p = 0.07). Mean ROC areas in the training and validation samples were 0.829 and 0.791 for replicate models of sample size 500, and 0.831 and 0.779 for models of sample size 250. There was no significant difference in ROC areas between training and validation cohorts for 80.5% of models of sample size 500, and for 75.3% of models of sample size 250. It is concluded that the most important predictor variables from a validated rule were included in the majority of replicate models, although the rule itself was reproduced in only a small minority of cases. In addition, the replicate models demonstrated good discriminatory power, and met statistical criteria for validation in an independent testing sample in a high percentage of cases.
