Figure 5.

Model-based CART analysis based on the biomarker GP73 conditional on the tree analysis using AFP, AAT, Kininogen and age, after controlling for site, for the complete data set. Variables that appear in the tree were involved in a statistically significant split (based on p-value < 0.05). Any two (or more) bins that appear at the bottom child nodes in this tree sharing the same mother node represent disjoint sub-groups of patients identified by this method to be (statistically) significantly different. The sub-groups are defined by the respective cut-points for biomarker levels and age. For example, when gender effect is controlled for in the model it is evident that age alone, independent of other biomarkers, plays a significant role in the incidence of HCC (p < 0.001). The node pair (1,9) represents the sub-group of 75 patients older than 61 years that have a significantly higher incidence of HCC compared to younger patients. It provides a unique, visual representation of complex interactions between biomarkers, age and gender though gender is not found to be statistically significant in any of the interactions. In addition, this approach identifies potential cut-points for biomarker levels that are significantly associated with the incidence of HCC. A detailed interpretation of this tree is provided in the text.

Wang et al. BMC Medical Genomics 2013 6(Suppl 3):S9   doi:10.1186/1755-8794-6-S3-S9