1 calcified plaque area was evaluated by using
recursive partitioning analysis.
2 on with the Radiation Therapy Oncology Group
Recursive Partitioning Analysis Brain Metastases Databas
3 ses from lung (52%) and breast (24%) cancer,
recursive partitioning analysis class 2 (96%), and an av
4 prognostic Radiation Therapy Oncology Group
recursive partitioning analysis class and geographic reg
5 nalysis showed that poor prognosis patients (
recursive partitioning analysis class V/VI) may derive a
6 isation for Research and Treatment of Cancer
recursive partitioning analysis class, MGMT promoter met
7 Patients were stratified by
recursive partitioning analysis class, number of brain m
8 on and T stage, N stage, stage grouping, and
recursive partitioning analysis classes (r = -0.07 to 0.
9 stage, N stage, combined stage grouping, and
recursive partitioning analysis classes.
10 ection, and Radiation Therapy Oncology Group
recursive partitioning analysis classification did not d
11 Recursive partitioning analysis confirmed portal hyperte
12 A novel nodal staging system derived by
recursive partitioning analysis exhibited greater concor
13 Recursive partitioning analysis indicates that involved
14 breast-GPA, multivariable Cox regression and
recursive partitioning analysis led to the development o
15 ndent risk factors for LD were included in a
recursive partitioning analysis model.
16 Recursive partitioning analysis more accurately identifi
17 Recursive partitioning analysis of initial PSA level, pa
18 Recursive partitioning analysis reinforced the prognosti
19 We used
recursive partitioning analysis (
RPA) and adjusted hazar
20 For HPV-related OPC,
recursive partitioning analysis (
RPA) derived new RPA st
21 Recursive partitioning analysis (
RPA) identified TTI thr
22 To refine the existing clinically based
recursive partitioning analysis (
RPA) model by incorpora
23 staged according to a model constructed by a
recursive partitioning analysis (
RPA) of glioma patients
24 Recursive partitioning analysis (
RPA) stratified the DM
25 Recursive partitioning analysis (
RPA) was used to create
26 Recursive partitioning analysis (
RPA) was used to derive
27 Multivariable logistic regression and
recursive partitioning analysis (
RPA) were performed to
28 Cox proportional hazards (PH) regression and
recursive partitioning analysis (
RPA) were performed to
29 Recursive partitioning analysis (
RPA), a method of build
30 Survival of these 16 patients, by
recursive partitioning analysis (
RPA), was 11.2, 13.3, a
31 By using
recursive partitioning analysis (
RPA), we developed new
32 ancer Institute of Canada trial 26981/22981 (
recursive partitioning analysis [
RPA] class III, 19 v 21
33 uding the Radiation Therapy Oncology Group's
recursive partitioning analysis (
RTOG-RPA) class.
34 Recursive partitioning analysis showed elevated rates of
35 We apply
recursive partitioning analysis to examine the relations
36 Recursive partitioning analysis was used to determine cu
37 Using
recursive-partitioning analysis,
we classified our patie
38 Recursive partitioning analysis yielded 4 TNM groups: st