Modeling major lung resection outcomes using classification trees and multiple imputation techniques

Modeling of operative risks associated with major lung resection is potentially inaccurate and inefficient because of incomplete observations for predictor variables (covariates). Missing values do not usually occur randomly, potentially introducing an important source of bias in modeling. Deletion of cases with missing data also results in loss of precision. The current study analyzes incomplete variables as potential predictors of outcomes after major lung resection using imputation techniques.