Significados de recursive partitioning en inglés

In a cross-validation study, recursive partitioning classified the samples with 84% accuracy.

In addition, unbiased recursive partitioning provided a direct way of identification of more homogeneous subgroups.

To stratify patients, recursive partitioning analysis was used.

Prognostic factors were analyzed using recursive partitioning method.

Further recursive partitioning analysis showed significant increases in risk for patients older than 55 years with mitoses and sheeting.

A recursive partitioning model was used to identify prognostic factors that were associated with event-free survival (EFS).

Results For all settings, overlapping confidence intervals indicated similar prediction accuracy of unbiased recursive partitioning to established statistical approaches.

The recursive partitioning was able to classify 241 (95%) of the patients.

Here, we propose a new framework for global model tests for polytomous Rasch models based on a model-based recursive partitioning algorithm.

Multiple logistic regression and recursive partitioning analyses were performed to generate a clinical prediction rule for identifying children with these injuries.

In recursive partitioning analyses, RA disease factors predicted work disability among older subjects, and functional limitation was the fourth most important factor.

The cut-off values for tumor size changes to predict survival were explored via tree based recursive partitioning and validated by external data.

Background: We evaluated the hierarchical risk groups for the estimated survival of WHO grade III glioma patients using recursive partitioning analysis (RPA).

Results: Multivariate modelling and cross-validated recursive partitioning identified several energy metabolites that, when combined with clinical variables, classified patients based on change in neurocognitive states.

These results were confirmed by application of recursive partitioning and amalgamation algorithms (RECPAM), which led to classification of the patients into four homogeneous subgroups.

Recursive partitioning was performed to determine appropriate cut-offs for significant continuous variables.