1 ent strategy were evaluated in multivariable
multinomial logistic regression.
2 valuated the association between the 2 using
multinomial logistic regression.
3 orical obesity status was predicted by using
multinomial logistic regression.
4 nse and EPS classification was identified by
multinomial logistic regression.
5 mm probing depth (PD) were determined using
multinomial logistic regression.
6 TW patterns were assessed using multivariate
multinomial logistic regression.
7 ic disease trajectories were evaluated using
multinomial logistic regression.
8 re examined in both bivariate analysis and a
multinomial logistic regression.
9 ith cross-validation-based tuning as well as
multinomial logistic regression.
10 rs for type 2 diabetes were determined using
multinomial logistic regression.
11 , and feeling lonely, sad or depressed using
multinomial logistic regression.
12 GERD, non-GERD, or EoE) were estimated using
multinomial logistic regression.
13 and dog and cat ownership in infancy, using
multinomial logistic regression.
14 nitive change categories were examined using
multinomial logistic regression.
15 Risk factors were modelled using
multinomial logistic regression.
16 tures of the 3 organisms were compared using
multinomial logistic regression.
17 r of siblings and AMD were assessed by using
multinomial logistic regression.
18 tization and risk factors were studied using
multinomial logistic regression.
19 fspring allergic disease were estimated with
multinomial logistic regressions.
20 ors and clinical outcome were analyzed using
multinomial logistic regressions.
21 In
multinomial logistic regression,
a model incorporating d
22 We used
multinomial logistic regression adjusted for age, sex, e
23 We used chi(2) analysis and
multinomial logistic regression (
adjusted for sex and st
24 Multinomial logistic regression (
adjusted for state or j
25 Mixed-effects
multinomial logistic regression,
adjusted for age and se
26 dds of increased out-of-pocket costs (survey
multinomial logistic regression,
adjusted odds ratios [O
27 to predict HCM status was performed by using
multinomial logistic regression adjusting for age, sex,
28 d Saint-Gallen fulfilment was analyzed using
multinomial logistic regression,
adjusting for clinicopa
29 se outcomes were then tested with the use of
multinomial logistic regression.
An ED, HF, and LFD dieta
30 We performed
multinomial logistic regression analyses adjusted for so
31 Multinomial logistic regression analyses examined associ
32 Multinomial logistic regression analyses indicated that
33 Multinomial logistic regression analyses indicated that
34 ope, pathological, and demographic data into
multinomial logistic regression analyses of mortality di
35 Uni- and multivariate
multinomial logistic regression analyses were applied to
36 Multinomial logistic regression analyses were conducted
37 loss of sexual activity were assessed using
multinomial logistic regression analyses.
38 Utilizing
multinomial logistic regression analysis (MLRA) and rece
39 Multivariable
multinomial logistic regression analysis adjusting for g
40 A first-order Markov model with
multinomial logistic regression analysis considered four
41 Multinomial logistic regression analysis identified peri
42 Multinomial logistic regression analysis showed that pre
43 We performed
multinomial logistic regression analysis to assess the w
44 We performed a multivariable
multinomial logistic regression analysis to estimate odd
45 We performed a
multinomial logistic regression analysis to estimate the
46 We used
multinomial logistic regression analysis to examine fact
47 Multinomial logistic regression analysis was conducted t
48 Multinomial logistic regression analysis was performed t
49 Multinomial logistic regression analysis was used to fit
50 est, Fisher's exact test, one way ANOVA, and
Multinomial logistic regression analysis were conducted
51 The Bivariate and
multinomial logistic regression analysis were conducted
52 ion in caregiver) variables were assessed by
multinomial logistic regression analysis.
53 positively charged amino acids, according to
multinomial logistic regression analysis.
54 e development of each asthma phenotype using
multinomial logistic regression analysis.
55 Multinomial logistic regression analyzed the relationshi
56 Multinomial logistic regression and Cox models with leas
57 reathlessness and mortality were analyzed by
multinomial logistic regression and Cox regression, resp
58 Multinomial logistic regression and general linear model
59 rtainly suboptimal care were identified with
multinomial logistic regression and generalized linear m
60 Multinomial logistic regression and linear regression we
61 tified using directed acyclic graph-informed
multinomial logistic regression and presented in odds ra
62 maging biomarkers with OI was examined using
multinomial logistic regression and simple linear regres
63 Multinomial logistic regressions and propensity score ma
64 Hospital characteristics (using
multinomial logistic regression)
and survival (using Cox
65 mes were examined using logistic regression,
multinomial logistic regression,
and linear regression.
66 We used
multinomial logistic regression,
and odds ratios (ORs) w
67 clinical characteristics was assessed using
multinomial logistic regression,
and variables associate
68 ression and relative risk ratios (RRRs) from
multinomial logistic regression are reported.
69 vents after starting treatment (P = .005, by
multinomial logistic regression)
but not death.
70 ample, analytic methods such as quantile and
multinomial logistic regression can describe the effects
71 Data were analyzed using
multinomial logistic regression controlling for age, gen
72 ion of glucose tolerance were assessed using
multinomial logistic regression corrected for familial c
73 nce of machine learning-gradient boosting vs
multinomial logistic regression differed only slightly (
74 Multinomial logistic regression estimated AHOs odds rati
75 Multinomial logistic regression estimated separate ORs f
76 days covered by 3-drug ART, and hierarchical
multinomial logistic regression estimated the risk of ne
77 Multinomial logistic regression-
estimated odds of MPD an
78 iduals with similar trajectories of AHA, and
multinomial logistic regression examined associations of
79 nding, and group-based trajectory models and
multinomial logistic regression examined patterns of and
80 s identified age-for-grade trajectories, and
multinomial logistic regression examined their associati
81 alized US adults aged 18 years or older, and
multinomial logistic regression examines whether variabl
82 Multinomial logistic regression for clustered data indic
83 ear regression for continuous phenotypes and
multinomial logistic regression for skeletal malocclusio
84 combination of semi-supervised technique and
multinomial logistic regression holds the potential to l
85 Multinomial logistic regression identified characteristi
86 icted cubic splines modeled temporal trends;
multinomial logistic regression identified sociodemograp
87 Multinomial logistic regression identified the most impo
88 identified age 24 inflammatory profiles and
multinomial logistic regressions identified associations
89 Multinomial logistic regressions in an interrupted time
90 However, adjusted
multinomial logistic regression indicates each unit incr
91 formances of five ML models (random forests,
multinomial logistic regression,
linear support vector c
92 ultiple models have been developed including
Multinomial Logistic Regression (
MLR) describing variant
93 sianNB (GNB), Support Vector Machines (SVM),
Multinomial Logistic Regression (
MLR), K-Nearest Neighbo
94 Using
multinomial logistic regression (
MLR), we compared the 3
95 sing resting-state fNIRS (rs-fNIRS) data and
multinomial logistic regression (
MLR), we identified bra
96 We further established a
multinomial logistic regression model for cell-type clas
97 Analyses included a
multinomial logistic regression model for early- and lat
98 A conservative penalized
multinomial logistic regression model identified 14 vari
99 A
multinomial logistic regression model showed that optoac
100 ve developed PyR(0), a hierarchical Bayesian
multinomial logistic regression model that infers relati
101 We used a multivariable
multinomial logistic regression model to estimate relati
102 hine (SETRED-SVM) model and an L2-penalized,
multinomial logistic regression model to obtain high con
103 uate vital registration system; we applied a
multinomial logistic regression model to vital registrat
104 A
multinomial logistic regression model was used to differ
105 A
multinomial logistic regression model was used to examin
106 A
multinomial logistic regression model was used to infer
107 A person-time
multinomial logistic regression model was used to simult
108 tes were analyzed using a first-order Markov
multinomial logistic regression model with 11 different
109 Our method is based on the
multinomial logistic regression model with a tree-guided
110 sed levels of HBD-2 (Pearson correlation and
multinomial logistic regression model).
111 In the
multinomial logistic regression model, a 10% increase in
112 In the adjusted
multinomial logistic regression model, a serum bicarbona
113 In a
multinomial logistic regression model, several factors s
114 Using a
multinomial logistic regression model, we estimated grow
115 ables and visit type were determined using a
multinomial logistic regression model.
116 high-incidence) as dependent variables in a
multinomial logistic regression model.
117 R: 0.85; 95% CI: 0.54, 1.34) in the adjusted
multinomial logistic regression model.
118 on behavior were analyzed in a multivariable
multinomial logistic regression model.
119 PTSD-depression concurrent) were examined by
multinomial logistic regression modeling (resilient stat
120 iles of comorbid symptoms, and multivariable
multinomial logistic regression modeling examined associ
121 Multinomial logistic regression modeling indicated that
122 Multinomial logistic regression modeling was performed t
123 Adjusted
multinomial logistic regressions modelled pathology-NPS
124 Multinomial logistic regression models (MLRM) combining
125 02) but not after 6- and 9-y of follow-up in
multinomial logistic regression models adjusted for base
126 We used sex-specific linear and
multinomial logistic regression models adjusted for demo
127 Using
multinomial logistic regression models adjusted for pati
128 sion models and as a categorical variable in
multinomial logistic regression models adjusted for sex,
129 s of BMI and WHR with DR were assessed using
multinomial logistic regression models adjusting for age
130 Rs) and 95% confidence intervals (CIs) using
multinomial logistic regression models adjusting for pot
131 All linear mixed and
multinomial logistic regression models controlled for ag
132 Data were analyzed with logistic and
multinomial logistic regression models controlling for d
133 Multinomial logistic regression models estimated the ass
134 Multinomial logistic regression models examined the asso
135 us gastroenteritis (RVGE) using binomial and
multinomial logistic regression models for non-specific
136 Results from adjusted
multinomial logistic regression models indicated that re
137 Modified Poisson and
multinomial logistic regression models quantified relati
138 We fit
multinomial logistic regression models to assess associa
139 We used adjusted
multinomial logistic regression models to estimate odds
140 We used
multinomial logistic regression models to explore the ps
141 Multinomial logistic regression models were analyzed com
142 Multinomial logistic regression models were constructed
143 Multinomial logistic regression models were fit to deter
144 al-odds generalized ordered logit models and
multinomial logistic regression models were fit to inves
145 Multivariable-adjusted
multinomial logistic regression models were performed to
146 Two
multinomial logistic regression models were used to anal
147 In addition,
multinomial logistic regression models were used to asse
148 Multinomial logistic regression models were used to asse
149 was used to identify trajectory groups, and
multinomial logistic regression models were used to char
150 Multinomial logistic regression models were used to comp
151 Modified Poisson and
multinomial logistic regression models were used to deri
152 Multinomial logistic regression models were used to esta
153 Multivariable and
multinomial logistic regression models were used to esti
154 Multinomial logistic regression models were used to exam
155 Multinomial logistic regression models were used to exam
156 Multinomial logistic regression models were used to inve
157 Multinomial logistic regression models with inverse prob
158 We used generalized linear and
multinomial logistic regression models with random inter
159 We fit mixed-effects
multinomial logistic regression models with the center a
160 marker levels were assessed using linear and
multinomial logistic regression models, respectively.
161 atus and genetic ancestry using logistic and
multinomial logistic regression models.
162 ffect of baseline factors was assessed using
multinomial logistic regression models.
163 pollutants and stage of BC were assessed by
multinomial logistic regression models.
164 n vaginal community state types (CSTs) using
multinomial logistic regression models.
165 esonance imaging using linear regression and
multinomial logistic regression models.
166 imated through relative risk ratios (RRR) by
multinomial logistic regression models.
167 These were used as covariates in 10
multinomial logistic regression models.
168 Calibrators included ridge-penalized
multinomial logistic regression (
MR) and Platt scaling b
169 Subsequent
multinomial logistic regression,
MultiPhen and Random Fo
170 odels and techniques such as Decision Trees,
Multinomial Logistic Regression,
Naive Bayes, k-Nearest
171 from immediate graft function recipients in
multinomial logistic regression (
odds ratio, 0.77; P<0.0
172 Using
multinomial logistic regression,
odds ratios (ORs) and 9
173 Logistic and
multinomial logistic regression of outcomes, estrogen re
174 Logistic and
multinomial logistic regression of the data were conduct
175 Multinomial logistic regression provides an attractive f
176 Multinomial logistic regressions (
reference: low risk) a
177 sub-outcomes was conducted using binary and
multinomial logistic regression,
respectively.
178 up-based trajectory models and multivariable
multinomial logistic regression,
respectively.
179 sing 2-level logistic regression and 2-level
multinomial logistic regression,
respectively.
180 ome quintile) were assessed using linear and
multinomial logistic regressions,
respectively.
181 oncentrations (>/=14 ng/L) using Poisson and
multinomial logistic regressions,
respectively.
182 ar OUD who used buprenorphine, multivariable
multinomial logistic regression results indicated that b
183 Multinomial logistic regression revealed that being with
184 Multinomial logistic regression revealed that the increa
185 Using
multinomial logistic regression,
risk ratios of > +0.5 d
186 Multinomial logistic regression showed that country, age
187 The results of
multinomial logistic regression showed that men with HTN
188 Multinomial logistic regression showed that patient risk
189 Fixed effects
multinomial logistic regression showed that shortened di
190 Place of death was compared using adjusted
multinomial logistic regressions stratified by payer and
191 Using
multinomial logistic regression,
the authors found that
192 Using
multinomial logistic regression,
the study identified fa
193 kcal from UPFs, and multivariable linear and
multinomial logistic regression to assess the associatio
194 We used
multinomial logistic regression to assess whether charac
195 We use
multinomial logistic regression to correlate the yearly
196 We use
multinomial logistic regression to develop separate equa
197 data from 5,653 adults using survey-weighted
multinomial logistic regression to estimate associations
198 We used
multinomial logistic regression to estimate unadjusted a
199 We used
multinomial logistic regression to evaluate associations
200 We used
multinomial logistic regression to evaluate associations
201 We used
multinomial logistic regression to evaluate the relation
202 g hemodialysis in the United States, we used
multinomial logistic regression to evaluate whether prio
203 n of FPE, followed by bivariate analyses and
multinomial logistic regression to examine associations
204 We used
multinomial logistic regression to generate covariates o
205 We used
multinomial logistic regression to identify baseline fac
206 a to identify linear growth trajectories and
multinomial logistic regression to identify covariates t
207 We also used
multinomial logistic regression to identify factors asso
208 We performed
multinomial logistic regression to identify predictors i
209 pplied four machine learning classifiers and
multinomial logistic regression to the titre data to pre
210 lications and over 155 sites, we construct a
multinomial logistic regression using Bayesian Hamiltoni
211 ng sleeping were assessed by cross-sectional
multinomial logistic regression using standardized proto
212 cause-specific mortality fractions applying
multinomial logistic regressions using adequate VR for l
213 Multinomial logistic regression was conducted to investi
214 s participants made, a hierarchical bayesian
multinomial logistic regression was fit to derive mean i
215 Multinomial logistic regression was performed to compare
216 Multinomial logistic regression was performed to examine
217 f distress and depression were examined, and
multinomial logistic regression was performed.
218 A
multinomial logistic regression was then used to predict
219 Penalized
multinomial logistic regression was used for validation.
220 Machine learning using
multinomial logistic regression was used in the training
221 Machine learning using
multinomial logistic regression was used in the training
222 Machine learning using
multinomial logistic regression was used in the training
223 Machine learning using
multinomial logistic regression was used in the training
224 Machine learning using
multinomial logistic regression was used in the training
225 Machine learning using
multinomial logistic regression was used in the training
226 Machine learning using
multinomial logistic regression was used in the training
227 Machine learning using
multinomial logistic regression was used on the training
228 Machine learning using
multinomial logistic regression was used on the training
229 Machine learning using
multinomial logistic regression was used on the training
230 Machine learning using
multinomial logistic regression was used on the training
231 Machine learning using
multinomial logistic regression was used on the training
232 Machine learning using
multinomial logistic regression was used on the training
233 Multinomial logistic regression was used to analyse the
234 In addition,
multinomial logistic regression was used to analyze risk
235 Multinomial logistic regression was used to analyze the
236 Multinomial logistic regression was used to ascertain fa
237 Multinomial logistic regression was used to assess the i
238 Multinomial logistic regression was used to compare char
239 Multinomial logistic regression was used to compare the
240 Multinomial logistic regression was used to determine as
241 Multinomial logistic regression was used to determine as
242 Multinomial logistic regression was used to determine de
243 Logistic and
multinomial logistic regression was used to determine th
244 Multinomial logistic regression was used to determine th
245 An interrupted time series analysis with
multinomial logistic regression was used to determine wh
246 Weighted
multinomial logistic regression was used to estimate ass
247 Multinomial logistic regression was used to estimate ORs
248 Multivariate
multinomial logistic regression was used to estimate the
249 Multinomial logistic regression was used to estimate the
250 Multinomial logistic regression was used to evaluate fac
251 Multinomial logistic regression was used to evaluate the
252 Multinomial logistic regression was used to examine fact
253 Multinomial logistic regression was used to examine the
254 Multinomial logistic regression was used to identify bas
255 Multinomial logistic regression was used to identify pot
256 Multinomial logistic regression was used to identify pot
257 Multinomial logistic regression was used to investigate
258 Multinomial logistic regression was used to report unadj
259 Multinomial logistic regression was used to test the ass
260 Multinomial logistic regression was used to test the ass
261 Penalized
multinomial logistic regression was utilized to create a
262 Using
multinomial logistic regression,
we determined factors a
263 Using
multinomial logistic regression,
we examined the associa
264 Using metabolite profiling and
multinomial logistic regression,
we identified pivotal m
265 Using weighted
multinomial logistic regression,
we modeled each barrier
266 ANOVA comparison and adjusted
multinomial logistic regression were used to evaluate cl
267 Descriptive statistics and
multinomial logistic regression were used to explore mat
268 Univariate analysis and multivariable
multinomial logistic regressions were conducted to evalu
269 Multivariable logistic and
multinomial logistic regressions were used to assess the
270 Multivariable
multinomial logistic regressions were used to cross-sect
271 were estimated in a hip-based analysis using
multinomial logistic regression with adjustment for age,
272 Risk was assessed through multivariable and
multinomial logistic regression with adjustment for rele
273 We used
multinomial logistic regression with clustered SEs to es
274 Machine learning used
multinomial logistic regression with lasso regularizatio
275 Using
multinomial logistic regression with psychiatrists as a
276 equate vital registration; we used a similar
multinomial logistic regression with verbal autopsy data
277 luated using weighted uni- and multi-variate
multinomial logistic regressions (
with no periodontitis