Scoring and staging systems using cox linear regression modeling and recursive partitioning.
OBJECTIVE
Scoring and staging systems are used to determine the order and class of data according to predictors. Systems used for medical data, such as the Child-Turcotte-Pugh scoring and staging systems for ordering and classifying patients with liver disease, are often derived strictly from physicians' experience and intuition. We construct objective and data-based scoring/staging systems using statistical methods.


METHODS
We consider Cox linear regression modeling and recursive partitioning techniques for censored survival data. In particular, to obtain a target number of stages we propose cross-validation and amalgamation algorithms. We also propose an algorithm for constructing scoring and staging systems by integrating local Cox linear regression models into recursive partitioning, so that we can retain the merits of both methods such as superior predictive accuracy, ease of use, and detection of interactions between predictors. The staging system construction algorithms are compared by cross-validation evaluation of real data.


RESULTS
The data-based cross-validation comparison shows that Cox linear regression modeling is somewhat better than recursive partitioning when there are only continuous predictors, while recursive partitioning is better when there are significant categorical predictors. The proposed local Cox linear recursive partitioning has better predictive accuracy than Cox linear modeling and simple recursive partitioning.


CONCLUSIONS
This study indicates that integrating local linear modeling into recursive partitioning can significantly improve prediction accuracy in constructing scoring and staging systems.
Department of Statistics, Korea University, Seoul, Korea.
J W
JW
Lee
S H
SH
Um
J B
JB
Lee
J
J
Mun
H
H
Cho
D006801:Humans; D008107:Liver Diseases / Q000145:classification; D008490:Medical Informatics / Q000706:statistics & numerical data*; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D016019:Survival Analysis