Fig 2.

Multivariable contributions of health measures to COPCs in OPPERA-2 (n = 655 participants). Random forest modeling explored the multivariable contributions of all health measures to each binary COPC case classification, with study site, age, gender, and race also included as covariates. Contributions of individual variables in the random forest models were quantified using variable importance scores, which estimate the relative contribution of each predictor to the model’s classification of true positives and true negatives. Other health measures were included in the models, but are excluded from the figure due to negligible variable importance scores. The threshold for exclusion from the figure was set to 0.0004 to ensure a clear, concise plot. A variable importance score < 0.0004 means that in the presence of all the other measures included in the random forest model, these health measures improved the misclassification error rate by less than 0.04 percentage points. Filled symbols = COPC cases; open symbols = controls.