1 he CBPS with logistic regression and boosted
classification and regression trees.
2 misspecified logistic PS models and boosted
classification and regression trees.
3 Analysis was conducted by
classification and regression trees,
a nonparametric mod
4 The
classification and regression tree algorithm had 92.3% s
5 In the validation study, the
classification and regression tree algorithm had overall
6 A
classification and regression tree algorithm with additi
7 atic analysis using ProPeak as well as CART (
Classification and Regression Tree)
algorithms to identi
8 Classification and regression tree analyses did not iden
9 ent multifactor dimensionality reduction and
classification and regression tree analyses indicated th
10 Multivariate logistic regression and
classification and regression tree analyses revealed tha
11 Cox regression and
classification and regression tree analyses were used to
12 bjects in the training set were subjected to
classification and regression tree analyses, through whi
13 Logistic regression and
classification and regression tree analysis (CART) were
14 Using
classification and regression tree analysis and demograp
15 Classification and regression tree analysis demonstrated
16 urvivors (LTS; >or= 36 months), and explored
classification and regression tree analysis for survival
17 Classification and regression tree analysis further reve
18 Classification and regression tree analysis identified a
19 Classification and regression tree analysis in a trainin
20 Classification and regression tree analysis revealed tha
21 Classification and regression tree analysis stratified p
22 variables, which were then incorporated in a
classification and regression tree analysis to construct
23 A
classification and regression tree analysis was performe
24 A
Classification and Regression Tree analysis was performe
25 The
Classification and Regression Tree analysis was performe
26 Classification and regression tree analysis was used to
27 Classification and regression tree analysis was used to
28 Classification and regression tree analysis was used to
29 Multivariate
classification and regression tree analysis were used to
30 finitions of improvement were developed from
classification and regression tree analysis, a data-mini
31 In a
classification and regression tree analysis, age older t
32 In a risk stratification approach with
classification and regression tree analysis, combined LV
33 On
Classification and Regression Tree analysis, sex was the
34 diagnostic criterion was formulated by using
classification and regression tree analysis.
35 categorized into risk groups on the basis of
classification and regression tree analysis.
36 using Cox proportional hazards modeling and
classification and regression tree analysis.
37 We used
classification-and-regression-tree analysis to estimate
38 hout (n = 211) lung cancer were subjected to
classification and regression tree and logistic regressi
39 Classification and regression tree and random forest ana
40 ormed by using Bland-Altman regression tree,
classification and regression tree,
and Shapiro-Wilk nor
41 first 24 hours of admission and then using a
Classification and Regression Tree approach to estimate
42 independently associated with the flare and
classification and regression tree approaches were devel
43 hat a data mining prediction model using the
classification and regression tree (
CART) algorithm can
44 ve, Kaplan-Meier method, Cox regression, and
classification and regression tree (
CART) analyses were
45 In addition, non-parametric
Classification and Regression Tree (
CART) analyses were
46 Classification and regression tree (
CART) analysis and l
47 Classification and regression tree (
CART) analysis ident
48 Classification and Regression Tree (
CART) analysis selec
49 Classification and regression tree (
CART) analysis was u
50 Classification and Regression Tree (
CART) analysis was u
51 Classification and regression tree (
CART) analysis was u
52 A
classification and regression tree (
CART) analysis was u
53 Classification and regression tree (
CART) analysis was u
54 umber of teeth, and oral health status), and
classification and regression tree (
CART) analysis.
55 e higher-order gene-gene interactions, using
classification and regression tree (
CART) analysis.
56 A
Classification and Regression Tree (
CART) procedure is i
57 ied 42 (67%) as direct ERalpha targets using
classification and regression tree (
CART) statistical mo
58 multifactor dimensionality reduction (MDR),
classification and regression tree (
CART), and tradition
59 shrinkage and selection operator (LASSO) and
classification and regression tree (
CART).
60 Cox regression, logistic regression, and
classification and regression trees (
CART) analyses were
61 Multiple regression and
classification and regression trees (
CART) analyses were
62 Classification and Regression Trees (
CART) analysis of a
63 Standard logistic regression analysis and
classification and regression trees (
CART) analysis were
64 Random Forest, an ensemble approach based on
classification and regression trees (
CART).
65 Recursive partitioning methods (using the
Classification and Regression Trees [
CART] program) were
66 extends the binary tree-structured approach (
Classification and Regression Trees,
CART) although it d
67 Classification and regression trees have long been used
68 atification model for 28-day mortality using
classification and regression tree methodology (n = 307)
69 We achieved risk stratification using
Classification and Regression Tree methodology.
70 ycin-resistant Enterococcus, a four-variable
classification and regression tree model (intravenous an
71 We use a
classification and regression tree model to further refi
72 A
classification and regression tree model with six of the
73 factors among the EAEC strains, coupled with
classification and regression tree modeling to reveal co
74 Time-series
classification and regression tree models based on BSI w
75 We have developed and validated
Classification and Regression Tree models that predict s
76 (n = 38,092) were used to develop candidate
Classification and Regression Trees models to predict th
77 ively (P < .001) with the sensitivity of the
classification and regression tree rule, which was 75% i
78 ch eye and a machine learning algorithm, the
classification and regression tree,
was used to classify
79 By using
classification and regression trees,
we identified the k
80 Classification and Regression Trees were used to develop