1 calcified plaque area was evaluated by using
recursive partitioning analysis.
2 rvival (OS) and progression-free survival by
recursive partitioning analysis.
3 toff values of biomarkers were identified by
recursive partitioning analysis.
4 idated based on these variables using binary
recursive partitioning analysis.
5 on with the Radiation Therapy Oncology Group
Recursive Partitioning Analysis Brain Metastases Databas
6 urrent or second primary HNSCC who satisfied
recursive partitioning analysis class 1 and 2 definition
7 ses from lung (52%) and breast (24%) cancer,
recursive partitioning analysis class 2 (96%), and an av
8 prognostic Radiation Therapy Oncology Group
recursive partitioning analysis class and geographic reg
9 nalysis showed that poor prognosis patients (
recursive partitioning analysis class V/VI) may derive a
10 isation for Research and Treatment of Cancer
recursive partitioning analysis class, MGMT promoter met
11 Patients were stratified by
recursive partitioning analysis class, number of brain m
12 on and T stage, N stage, stage grouping, and
recursive partitioning analysis classes (r = -0.07 to 0.
13 stage, N stage, combined stage grouping, and
recursive partitioning analysis classes.
14 ection, and Radiation Therapy Oncology Group
recursive partitioning analysis classification did not d
15 Recursive partitioning analysis confirmed portal hyperte
16 Recursive partitioning analysis determined ADC and mDCE
17 Four were selected via
recursive partitioning analysis:
epidural tumor extensio
18 A novel nodal staging system derived by
recursive partitioning analysis exhibited greater concor
19 Recursive partitioning analysis identified 3 features (s
20 Recursive partitioning analysis identified the number of
21 Recursive partitioning analysis identified three surviva
22 Recursive partitioning analysis indicates that involved
23 breast-GPA, multivariable Cox regression and
recursive partitioning analysis led to the development o
24 Adjusted hazard ratios (AHRs) and
recursive partitioning analysis methods were applied to
25 ndent risk factors for LD were included in a
recursive partitioning analysis model.
26 Recursive partitioning analysis more accurately identifi
27 Recursive partitioning analysis of initial PSA level, pa
28 Recursive partitioning analysis reinforced the prognosti
29 The
recursive partitioning analysis revealed that tumor size
30 We used
recursive partitioning analysis (
RPA) and adjusted hazar
31 d nivo) or temozolomide (TMZ), stratified by
recursive partitioning analysis (
RPA) class and intentio
32 For HPV-related OPC,
recursive partitioning analysis (
RPA) derived new RPA st
33 Recursive partitioning analysis (
RPA) identified TTI thr
34 To refine the existing clinically based
recursive partitioning analysis (
RPA) model by incorpora
35 staged according to a model constructed by a
recursive partitioning analysis (
RPA) of glioma patients
36 Recursive partitioning analysis (
RPA) stratified the DM
37 A
recursive partitioning analysis (
RPA) was performed, as
38 Recursive partitioning analysis (
RPA) was used to create
39 Recursive partitioning analysis (
RPA) was used to derive
40 Recursive partitioning analysis (
RPA) was used to group
41 Multivariable logistic regression and
recursive partitioning analysis (
RPA) were performed to
42 Cox proportional hazards (PH) regression and
recursive partitioning analysis (
RPA) were performed to
43 Recursive partitioning analysis (
RPA), a method of build
44 Survival of these 16 patients, by
recursive partitioning analysis (
RPA), was 11.2, 13.3, a
45 By using
recursive partitioning analysis (
RPA), we developed new
46 odal classification system was derived using
recursive partitioning analysis (
RPA).
47 ancer Institute of Canada trial 26981/22981 (
recursive partitioning analysis [
RPA] class III, 19 v 21
48 uding the Radiation Therapy Oncology Group's
recursive partitioning analysis (
RTOG-RPA) class.
49 Recursive partitioning analysis selected an ADC cutoff o
50 Recursive partitioning analysis showed elevated rates of
51 Recursive partitioning analysis showed that IFN-gamma di
52 We apply
recursive partitioning analysis to examine the relations
53 Recursive partitioning analysis was conducted using sepa
54 A
recursive partitioning analysis was performed to evaluat
55 Exploratory
recursive partitioning analysis was then used to model a
56 Recursive partitioning analysis was used to determine cu
57 Recursive partitioning analysis was used to group patien
58 Using cut-points from
recursive partitioning analysis,
we derived a 5-miRNA si
59 Using
recursive-partitioning analysis,
we classified our patie
60 Recursive partitioning analysis yielded 4 TNM groups: st