1 nse and EPS classification was identified by
multinomial logistic regression.
2 nitive change categories were examined using
multinomial logistic regression.
3 Risk factors were modelled using
multinomial logistic regression.
4 tures of the 3 organisms were compared using
multinomial logistic regression.
5 r of siblings and AMD were assessed by using
multinomial logistic regression.
6 tization and risk factors were studied using
multinomial logistic regression.
7 valuated the association between the 2 using
multinomial logistic regression.
8 orical obesity status was predicted by using
multinomial logistic regression.
9 fspring allergic disease were estimated with
multinomial logistic regressions.
10 ors and clinical outcome were analyzed using
multinomial logistic regressions.
11 dds of increased out-of-pocket costs (survey
multinomial logistic regression,
adjusted odds ratios [O
12 se outcomes were then tested with the use of
multinomial logistic regression.
An ED, HF, and LFD dieta
13 We performed
multinomial logistic regression analyses adjusted for so
14 Multinomial logistic regression analyses indicated that
15 loss of sexual activity were assessed using
multinomial logistic regression analyses.
16 Multinomial logistic regression analysis identified peri
17 We performed
multinomial logistic regression analysis to assess the w
18 We performed a multivariable
multinomial logistic regression analysis to estimate odd
19 We performed a
multinomial logistic regression analysis to estimate the
20 Multinomial logistic regression analysis was performed t
21 positively charged amino acids, according to
multinomial logistic regression analysis.
22 e development of each asthma phenotype using
multinomial logistic regression analysis.
23 maging biomarkers with OI was examined using
multinomial logistic regression and simple linear regres
24 Multinomial logistic regressions and propensity score ma
25 Hospital characteristics (using
multinomial logistic regression)
and survival (using Cox
26 ample, analytic methods such as quantile and
multinomial logistic regression can describe the effects
27 Data were analyzed using
multinomial logistic regression controlling for age, gen
28 ion of glucose tolerance were assessed using
multinomial logistic regression corrected for familial c
29 Multinomial logistic regression estimated AHOs odds rati
30 Multinomial logistic regression estimated separate ORs f
31 alized US adults aged 18 years or older, and
multinomial logistic regression examines whether variabl
32 Multinomial logistic regression for clustered data indic
33 ear regression for continuous phenotypes and
multinomial logistic regression for skeletal malocclusio
34 Analyses included a
multinomial logistic regression model for early- and lat
35 A conservative penalized
multinomial logistic regression model identified 14 vari
36 uate vital registration system; we applied a
multinomial logistic regression model to vital registrat
37 A
multinomial logistic regression model was used to differ
38 A
multinomial logistic regression model was used to infer
39 A person-time
multinomial logistic regression model was used to simult
40 tes were analyzed using a first-order Markov
multinomial logistic regression model with 11 different
41 Our method is based on the
multinomial logistic regression model with a tree-guided
42 sed levels of HBD-2 (Pearson correlation and
multinomial logistic regression model).
43 In the adjusted
multinomial logistic regression model, a serum bicarbona
44 high-incidence) as dependent variables in a
multinomial logistic regression model.
45 R: 0.85; 95% CI: 0.54, 1.34) in the adjusted
multinomial logistic regression model.
46 on behavior were analyzed in a multivariable
multinomial logistic regression model.
47 iles of comorbid symptoms, and multivariable
multinomial logistic regression modeling examined associ
48 02) but not after 6- and 9-y of follow-up in
multinomial logistic regression models adjusted for base
49 We used sex-specific linear and
multinomial logistic regression models adjusted for demo
50 Using
multinomial logistic regression models adjusted for pati
51 sion models and as a categorical variable in
multinomial logistic regression models adjusted for sex,
52 s of BMI and WHR with DR were assessed using
multinomial logistic regression models adjusting for age
53 Data were analyzed with logistic and
multinomial logistic regression models controlling for d
54 Multinomial logistic regression models estimated the ass
55 Multinomial logistic regression models examined the asso
56 Results from adjusted
multinomial logistic regression models indicated that re
57 We used adjusted
multinomial logistic regression models to estimate odds
58 Two
multinomial logistic regression models were used to anal
59 Multinomial logistic regression models were used to asse
60 Multinomial logistic regression models were used to comp
61 Multinomial logistic regression models were used to exam
62 Multinomial logistic regression models were used to exam
63 ffect of baseline factors was assessed using
multinomial logistic regression models.
64 esonance imaging using linear regression and
multinomial logistic regression models.
65 imated through relative risk ratios (RRR) by
multinomial logistic regression models.
66 These were used as covariates in 10
multinomial logistic regression models.
67 Subsequent
multinomial logistic regression,
MultiPhen and Random Fo
68 from immediate graft function recipients in
multinomial logistic regression (
odds ratio, 0.77; P<0.0
69 Logistic and
multinomial logistic regression of outcomes, estrogen re
70 Logistic and
multinomial logistic regression of the data were conduct
71 Multinomial logistic regression provides an attractive f
72 oncentrations (>/=14 ng/L) using Poisson and
multinomial logistic regressions,
respectively.
73 Multinomial logistic regression revealed that being with
74 Multinomial logistic regression showed that country, age
75 Using
multinomial logistic regression,
the authors found that
76 We used
multinomial logistic regression to assess whether charac
77 We used
multinomial logistic regression to estimate unadjusted a
78 We used
multinomial logistic regression to evaluate the relation
79 a to identify linear growth trajectories and
multinomial logistic regression to identify covariates t
80 cause-specific mortality fractions applying
multinomial logistic regressions using adequate VR for l
81 Multinomial logistic regression was performed to compare
82 f distress and depression were examined, and
multinomial logistic regression was performed.
83 A
multinomial logistic regression was then used to predict
84 Multinomial logistic regression was used to ascertain fa
85 Multinomial logistic regression was used to assess the i
86 Multinomial logistic regression was used to determine as
87 Multinomial logistic regression was used to determine de
88 Multinomial logistic regression was used to determine th
89 Logistic and
multinomial logistic regression was used to determine th
90 Multinomial logistic regression was used to estimate the
91 Multinomial logistic regression was used to evaluate fac
92 Multinomial logistic regression was used to evaluate the
93 Multinomial logistic regression was used to examine fact
94 Multinomial logistic regression was used to examine the
95 Multinomial logistic regression was used to identify bas
96 Multinomial logistic regression was used to identify pot
97 Multinomial logistic regression was used to identify pot
98 Multinomial logistic regression was used to investigate
99 Multinomial logistic regression was used to report unadj
100 Multinomial logistic regression was used to test the ass
101 Penalized
multinomial logistic regression was utilized to create a
102 Using
multinomial logistic regression,
we examined the associa
103 Using weighted
multinomial logistic regression,
we modeled each barrier
104 Descriptive statistics and
multinomial logistic regression were used to explore mat
105 were estimated in a hip-based analysis using
multinomial logistic regression with adjustment for age,
106 Risk was assessed through multivariable and
multinomial logistic regression with adjustment for rele
107 equate vital registration; we used a similar
multinomial logistic regression with verbal autopsy data