Summary
Objectives:
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.
Keywords
Child-Turcotte-Pugh - tree-structured method - censored survival data - local linear
model - cross-validation
