1 ed according to patient characteristics with
multiple logistic regression.
2 as descriptive and correlation analyses and
multiple logistic regression.
3 minants of readmission were identified using
multiple logistic regression.
4 offspring peanut allergy were examined using
multiple logistic regression.
5 s at baseline and week 4 were analyzed using
multiple logistic regression.
6 mens we developed the prediction model using
multiple logistic regression.
7 s (aCFSs) to that of nonfragile sites, using
multiple logistic regression.
8 ary outcome measures and were analyzed using
multiple logistic regression.
9 the APOE and MAPT loci, via cross-validated
multiple logistic regression.
10 aracteristics, and laboratory values using a
multiple logistic regression.
11 ls for association with lung cancer by using
multiple logistic regression.
12 -related hospitalization were examined using
multiple logistic regression.
13 ficant correlation by using chi(2) tests and
multiple logistic regression.
14 tests adjusted for multiple comparisons, and
multiple logistic regression.
15 d BRCAPRO models were assessed with stepwise
multiple logistic regression.
16 the Fisher exact test, unpaired t tests, and
multiple logistic regression.
17 dy (Virahep-C) were modeled using simple and
multiple logistic regression.
18 ia-adenocarcinoma sequence were estimated by
multiple logistic regression.
19 h sociodemographic variables was assessed by
multiple logistic regression.
20 es were analyzed using bivariate methods and
multiple logistic regression.
21 ations with each preterm birth outcome using
multiple logistic regression.
22 d at age of 6.5 years) were determined using
multiple logistic regression.
23 Statistical analysis included
multiple logistic regression.
24 rameters were assessed by means of linear or
multiple logistic regressions.
25 In
multiple logistic regression,
a TFdi,max less than 38.1%
26 s of SNPs on periodontitis were tested using
multiple logistic regressions adjusted for covariates.
27 Multiple logistic regressions adjusting for age, sex, Am
28 tions among these variables were examined by
multiple logistic regression,
adjusting for other CAD ri
29 in D and food allergy were examined by using
multiple logistic regression,
adjusting for potential ri
30 ir impact on outcome, using forward stepwise
multiple logistic regression after adjusting for known p
31 Smooth curve fitting and
multiple logistic regression analyses adjusting for age,
32 iodontal variable was obtained from separate
multiple logistic regression analyses adjusting for the
33 dependent variables was assessed by weighted
multiple logistic regression analyses adjusting for the
34 Univariate and
multiple logistic regression analyses explored the progn
35 m weight retention at 6 mo were estimated by
multiple logistic regression analyses for women in 3 cat
36 Multiple logistic regression analyses identified three i
37 Multiple logistic regression analyses showed that matern
38 e, and gender were included as covariates in
multiple logistic regression analyses to calculate adjus
39 se, we used Kaplan-Meier, Cox regression and
multiple logistic regression analyses to investigate the
40 Simple and
multiple logistic regression analyses were conducted to
41 Multiple logistic regression analyses were conducted to
42 Univariate and
multiple logistic regression analyses were performed, an
43 Univariable and
multiple logistic regression analyses were performed, us
44 Weighted chi2 tests and
multiple logistic regression analyses were used to exami
45 omics were obtained for the entire lung, and
multiple logistic regression analyses with areas under t
46 Data were analyzed by linear and
multiple logistic regression analyses, and the Mann-Whit
47 In
multiple logistic regression analyses, independent deter
48 In adjusted
multiple logistic regression analyses, metabolic syndrom
49 Using
multiple logistic regression analyses, shared epitope al
50 ee periods were made by using univariate and
multiple logistic regression analyses.
51 Multiple logistic-regression analyses showed that DNI wa
52 Data analysis comprised
multiple logistic regression analysis (case-control stud
53 Multiple logistic regression analysis (with generalized
54 R 4.37, 95% CI 1.41-13.54 [P = 0.010]) using
multiple logistic regression analysis adjusting for age,
55 By
multiple logistic regression analysis after adjusting fo
56 Multiple logistic regression analysis after inclusion of
57 Multiple logistic regression analysis assessed different
58 Multiple logistic regression analysis assessed the assoc
59 In
multiple logistic regression analysis baseline cardiac i
60 Multiple logistic regression analysis confirmed the cont
61 Multiple logistic regression analysis could not be done
62 Multiple logistic regression analysis demonstrated that
63 Multiple logistic regression analysis demonstrated that
64 A new
multiple logistic regression analysis demonstrated the a
65 Multiple logistic regression analysis evaluated the asso
66 Multiple logistic regression analysis for environmental
67 Multiple logistic regression analysis found that factors
68 Multiple logistic regression analysis identified inappro
69 Multiple logistic regression analysis identified non-idi
70 Multiple logistic regression analysis identified underly
71 Factors associated with depression via
multiple logistic regression analysis included younger a
72 Based on
multiple logistic regression analysis of PHS with adjust
73 Multiple logistic regression analysis of variables that
74 Multiple logistic regression analysis revealed an increa
75 Multiple logistic regression analysis revealed increased
76 Multiple logistic regression analysis revealed older age
77 Multiple logistic regression analysis revealed that chor
78 Multiple logistic regression analysis revealed that comb
79 Multiple logistic regression analysis revealed that male
80 Multiple logistic regression analysis revealed that the
81 Multiple logistic regression analysis revealed that, whe
82 Multiple logistic regression analysis showed that associ
83 Multiple logistic regression analysis showed that female
84 Multiple logistic regression analysis showed that high b
85 Multiple logistic regression analysis showed that increa
86 Multiple logistic regression analysis showed that infect
87 Multiple logistic regression analysis showed that the AL
88 Multiple logistic regression analysis showed that the pa
89 Multiple logistic regression analysis showed that the se
90 Multiple logistic regression analysis showed that visuos
91 Multiple logistic regression analysis showed that, after
92 sex, race/ethnicity, and geographic region;
multiple logistic regression analysis to determine indep
93 We performed
multiple logistic regression analysis to estimate the od
94 y, and vessel volumetry, were used to feed a
multiple logistic regression analysis to find significan
95 Multiple logistic regression analysis utilizing a linear
96 Multiple logistic regression analysis was applied to ide
97 When
multiple logistic regression analysis was applied, this
98 Multiple logistic regression analysis was conducted to d
99 Multiple logistic regression analysis was performed to d
100 Multiple logistic regression analysis was performed; gen
101 Multiple logistic regression analysis was used to assess
102 Multiple logistic regression analysis was used to determ
103 Multiple logistic regression analysis was used to estima
104 Multiple logistic regression analysis was used to examin
105 Stepwise
multiple logistic regression analysis was used to explor
106 Multiple logistic regression analysis was used to explor
107 Multiple logistic regression analysis was used to invest
108 Multiple logistic regression analysis was used to test f
109 k factors associated with eGFR <60 mL/min in
multiple logistic regression analysis were age (P < 0.00
110 and European QOL 5D Visual Analog Scale via
multiple logistic regression analysis were American regi
111 We performed
multiple logistic regression analysis, adjusting odds ra
112 In
multiple logistic regression analysis, being overweight
113 ion of the two miRNAs together, tested using
multiple logistic regression analysis, did not improve t
114 In
multiple logistic regression analysis, elderly recipient
115 In
multiple logistic regression analysis, NAFLD was the onl
116 In
multiple logistic regression analysis, only PTH increase
117 In
multiple logistic regression analysis, predicted extrava
118 On
multiple logistic regression analysis, significant predi
119 In
multiple logistic regression analysis, the prevalence of
120 By
multiple logistic regression analysis, we found that the
121 ce of at least one of the applicable PSIs on
multiple logistic regression analysis, with confirmation
122 ors for utilization of dental services using
multiple logistic regression analysis.
123 sted for a number of possible confounders in
multiple logistic regression analysis.
124 ween periodontal disease and PH on bivariate
multiple logistic regression analysis.
125 ociation between EBV and CAL was revealed by
multiple logistic regression analysis.
126 Independent factors were identified with
multiple logistic regression analysis.
127 those without CIN by using forward stepwise
multiple logistic regression analysis.
128 versus advanced fibrosis) were explored with
multiple logistic regression analysis.
129 e were independently associated with CDAD by
multiple logistic regression analysis.
130 ndependent of pack-year smoking history with
multiple logistic regression analysis.
131 associated with outcome were entered into a
multiple logistic regression analysis.
132 ountry's gross domestic product (GDP) with a
multiple logistic regression analysis.
133 e only independent predictor of mortality on
multiple logistic regression analysis.
134 In our
multiple logistic-regression analysis, consumption of ra
135 Bivariate
multiple logistic regression and adjusted prevalence ana
136 Multiple logistic regression and analysis of covariance
137 Multiple logistic regression and Cox proportional hazard
138 We used
multiple logistic regression and difference-in-differenc
139 Nonparametric tests as well as
multiple logistic regression and mixed effects logistic
140 binary nodule classification (T1/T2), using
multiple logistic regression and non-linear classifiers.
141 Associations were assessed by using
multiple logistic regression and subsequent meta-analysi
142 attributable fractions were derived by using
multiple logistic regression and the Levin formula.
143 developing severe renal insufficiency using
multiple logistic regression,
and the predictive ability
144 Multiple logistic regression assessed odds ratio for sur
145 In the
multiple logistic regressions,
BMI >=27.0 kg/m(2) , WC >
146 Multiple logistic regression (
c-statistic 0.715, 95% CI:
147 months before interview were obtained using
multiple logistic regression controlling for demographic
148 each microorganism with CAL was tested using
multiple logistic regressions controlling for age, smoki
149 28-day survivors, using Bonferroni-corrected
multiple logistic regression,
days alive and free of ven
150 Multiple logistic regression demonstrated an increased r
151 Multiple logistic regression demonstrated the following
152 Multiple logistic regressions demonstrated that an open
153 Using
multiple logistic regression,
five features were indepen
154 They used
multiple logistic regression for their comparison.
155 Multiple logistic regressions for the best 10% and the w
156 Multiple logistic regression identified having an OC, ag
157 Multiple logistic regression identified independent risk
158 rminants of neoatherosclerosis identified by
multiple logistic regression included younger age (p < 0
159 Using
multiple logistic regression,
increased eNO (odds ratio,
160 By
multiple logistic regression,
independent risk factors f
161 At
multiple logistic regression,
kurtosis on T2-weighted im
162 A
multiple logistic regression model (c-statistic 0.657, 9
163 to identify predictors of service use with a
multiple logistic regression model and predictors of cos
164 Multiple logistic regression model confirmed that endoth
165 ng deep neural networks as well as a simpler
multiple logistic regression model for classification of
166 A
multiple logistic regression model identified variables
167 A
multiple logistic regression model including standard Fr
168 A
multiple logistic regression model incorporating oxygena
169 A
multiple logistic regression model predicting odds of su
170 A
multiple logistic regression model revealed independent
171 A
multiple logistic regression model that included predict
172 In a
multiple logistic regression model the factor wound irri
173 found to be significant were entered into a
multiple logistic regression model to identify factors i
174 tooth-level multivariate survival model and
multiple logistic regression model using the method of g
175 A
multiple logistic regression model was estimated at impl
176 An adjusted
multiple logistic regression model was performed using e
177 An adjusted
multiple logistic regression model was performed using f
178 A
multiple logistic regression model was then developed to
179 A
multiple logistic regression model was used to evaluate
180 A
multiple logistic regression model was used to identify
181 Two types of a
multiple logistic regression model were fit: 1) logistic
182 Adjusted odds ratios (ORs) from a
multiple logistic regression model were used to estimate
183 s with bivariate analyses and constructing a
multiple logistic regression model with the number of po
184 In a
multiple logistic regression model, African American rac
185 pregnancy physical activity, and income in a
multiple logistic regression model, regular use of multi
186 In a
multiple logistic regression model, risks of incident hy
187 In a
multiple logistic regression model, the G allele was ass
188 In a
multiple logistic regression model, the OR for HLA-B*27:
189 r adjustment for significant covariates in a
multiple logistic regression model, the use of OSP was a
190 In a
multiple logistic regression model, there was a signific
191 ingle regressor analysis were entered into a
multiple logistic regression model.
192 iate predictors of death were entered into a
multiple logistic regression model.
193 analyzed thereafter in a backward selection
multiple logistic regression model.
194 Multiple logistic regression modeling and propensity sco
195 Multiple logistic regression modeling quantified the ass
196 Multiple logistic regression modeling was used to identi
197 Bivariate analyses and
multiple logistic regression modeling were performed.
198 ithout SAB and risk factors identified using
multiple logistic regression modeling.
199 In rejection episode analyses,
multiple logistic regression modelling showed that chang
200 By fitting three
multiple logistic regression models (one for each delive
201 Multiple logistic regression models adjusted for subject
202 Multiple logistic regression models analyzed all variabl
203 e best predicting parameter of CA diagnosis (
multiple logistic regression models P<0.00005 and P=0.00
204 Multiple logistic regression models revealed that combin
205 smoke exposure with ADHD was examined by two
multiple logistic regression models that differ in the s
206 We used
multiple logistic regression models to adjust for age, s
207 We used
multiple logistic regression models to examine the effec
208 Adjusted
multiple logistic regression models were applied to asse
209 Multiple logistic regression models were developed to ex
210 When
multiple logistic regression models were fit with adjust
211 Multiple logistic regression models were fitted to calcu
212 vidually assessed at the genotype level, and
multiple logistic regression models were used to adjust
213 patient characteristics was determined, and
multiple logistic regression models were used to adjust
214 Multiple logistic regression models were used to assess
215 nquired about "self-assessed periodontitis."
Multiple logistic regression models were used to constru
216 Multiple logistic regression models were used to examine
217 Multiple logistic regression models were used to identif
218 CI: 0.88, 1.02 (P = 0.16), respectively] in
multiple logistic regression models with adjustment for
219 ompliance were then evaluated in a series of
multiple logistic regression models with adjustment for
220 Data were analyzed using
multiple logistic regression models with backward stepwi
221 In
multiple logistic regression models, both treatment and
222 rvals (CIs) were obtained from unconditional
multiple logistic regression models, including terms for
223 In the
multiple logistic regression models, the median glycemic
224 Multiple logistic regression models, with tooth-level bl
225 laining 70% of the variance were included in
multiple logistic regression models.
226 e adjusted for patient characteristics using
multiple logistic regression models.
227 from 2002-2008 were examined in detail using
multiple logistic regression (
n = 774,399).
228 c-net was implemented to perform a penalized
multiple logistic regression on all biomarkers simultane
229 association was independently significant in
multiple logistic regression (
P = 0.04) along with race,
230 Multiple logistic regression revealed that the CYP11B2 -
231 Multiple logistic regressions revealed that both Fcgamma
232 Based on
multiple logistic regression,
RV/LV ratio, LV diameter,
233 Multiple logistic regression showed independent associat
234 Multiple logistic regression showed that all algorithm p
235 Multiple logistic regression showed that all algorithm p
236 Multiple logistic regression showed that the patients wh
237 Weighted
multiple logistic regressions showed that this relations
238 The
multiple logistic regression shows that as compared with
239 redictive than another using ROC curves, but
multiple logistic regression suggested salT was more pre
240 re compared between cases and controls using
multiple logistic regression techniques.
241 Student t test, chi(2), and
multiple logistic regression tests were performed as app
242 By
multiple logistic regressions,
the following association
243 al abnormalities and asbestos exposure using
multiple logistic regression to adjust for year of birth
244 We used
multiple logistic regression to assess differences in op
245 We used
multiple logistic regression to assess relationships bet
246 We used
multiple logistic regression to assess the association b
247 The authors used
multiple logistic regression to assess the relation betw
248 We used
multiple logistic regression to determine the independen
249 We used
multiple logistic regression to estimate associations be
250 We used
multiple logistic regression to estimate odds ratios (OR
251 We used
multiple logistic regression to estimate predictive marg
252 We used univariate and
multiple logistic regression to examine clinical and lab
253 We used
multiple logistic regression to examine how the presence
254 We used
multiple logistic regression to investigate whether mild
255 We used
multiple logistic regressions to evaluate clinical and s
256 In
multiple logistic regression,
transplant status was inde
257 In
multiple logistic regression,
urinary NGAL level was hig
258 Data were analyzed in a
multiple logistic regression using MoCA scores suggestiv
259 Multiple logistic regression using robust standard error
260 tors with P < 0.15 were analyzed by stepwise
multiple logistic regression,
using data stratified by P
261 When
multiple logistic regression was applied to the data, th
262 Multiple logistic regression was performed on demographi
263 Multiple logistic regression was performed to analyze th
264 Multiple logistic regression was performed to compare di
265 A
multiple logistic regression was performed to identify b
266 Multiple logistic regression was performed to study the
267 Multiple logistic regression was performed using a discr
268 ndent risk factors for hospital mortality by
multiple logistic regression was rupture (P<0.0009), and
269 Multiple logistic regression was used for probability an
270 Multiple logistic regression was used to assess the asso
271 Multiple logistic regression was used to assess the effe
272 Multiple logistic regression was used to assess the inde
273 Multiple logistic regression was used to compare anemia
274 Multiple logistic regression was used to determine the i
275 Multiple logistic regression was used to determine the r
276 Adjusting for student demographics,
multiple logistic regression was used to determine wheth
277 aseline who continued to drive at follow-up,
multiple logistic regression was used to estimate the od
278 Multiple logistic regression was used to examine associa
279 A
multiple logistic regression was used to explore the com
280 Multiple logistic regression was used to identify factor
281 Multiple logistic regression was used to identify factor
282 A
multiple logistic regression was used to identify indepe
283 Multiple logistic regression was used to identify indepe
284 Multiple logistic regression was used to identify risk f
285 Multiple logistic regression was used to investigate fac
286 Multiple logistic regression was used to measure the ass
287 Multiple logistic regression was used to measure the imp
288 Multiple logistic regression was used to quantify the ef
289 Multiple logistic regression was used to test hypotheses
290 Multiple logistic regression was used with mortality as
291 Using
multiple logistic regression,
we explored the associatio
292 Using
multiple logistic regression,
we identified significant
293 Descriptive statistical analysis and
multiple logistic regression were performed.
294 Random Forests classification and
multiple logistic regression were used to assess the RI
295 Weighted population, prevalence, and
multiple logistic regression were used.
296 Multiple logistic regressions were performed to evaluate
297 Bivariate analysis and
multiple logistic regressions were performed to identify
298 Multiple logistic regressions were used to derive adjust
299 rd deviation increase in log (undecane) in a
multiple logistic regression which also included vaginal
300 Multiple logistic regression,
with adjustments for demog