1 s at baseline and week 4 were analyzed using
multiple logistic regression.
2 s (aCFSs) to that of nonfragile sites, using
multiple logistic regression.
3 ary outcome measures and were analyzed using
multiple logistic regression.
4 aracteristics, and laboratory values using a
multiple logistic regression.
5 ls for association with lung cancer by using
multiple logistic regression.
6 -related hospitalization were examined using
multiple logistic regression.
7 ficant correlation by using chi(2) tests and
multiple logistic regression.
8 d at age of 6.5 years) were determined using
multiple logistic regression.
9 d BRCAPRO models were assessed with stepwise
multiple logistic regression.
10 the Fisher exact test, unpaired t tests, and
multiple logistic regression.
11 dy (Virahep-C) were modeled using simple and
multiple logistic regression.
12 ia-adenocarcinoma sequence were estimated by
multiple logistic regression.
13 ical variables were analyzed with the use of
multiple logistic regression.
14 rent atypical neuroleptics was examined with
multiple logistic regression.
15 Statistical analysis included
multiple logistic regression.
16 ed according to patient characteristics with
multiple logistic regression.
17 as descriptive and correlation analyses and
multiple logistic regression.
18 minants of readmission were identified using
multiple logistic regression.
19 offspring peanut allergy were examined using
multiple logistic regression.
20 rameters were assessed by means of linear or
multiple logistic regressions.
21 Multiple logistic regressions adjusting for age, sex, Am
22 tions among these variables were examined by
multiple logistic regression,
adjusting for other CAD ri
23 in D and food allergy were examined by using
multiple logistic regression,
adjusting for potential ri
24 ir impact on outcome, using forward stepwise
multiple logistic regression after adjusting for known p
25 iodontal variable was obtained from separate
multiple logistic regression analyses adjusting for the
26 dependent variables was assessed by weighted
multiple logistic regression analyses adjusting for the
27 Univariate and
multiple logistic regression analyses explored the progn
28 m weight retention at 6 mo were estimated by
multiple logistic regression analyses for women in 3 cat
29 Multiple logistic regression analyses identified three i
30 Ethnic-stratified
multiple logistic regression analyses showed divergent r
31 Multiple logistic regression analyses showed that matern
32 In
multiple logistic regression analyses that adjusted for
33 se, we used Kaplan-Meier, Cox regression and
multiple logistic regression analyses to investigate the
34 Simple and
multiple logistic regression analyses were conducted to
35 Multiple logistic regression analyses were conducted to
36 Multiple logistic regression analyses were done with eve
37 Univariate and
multiple logistic regression analyses were performed, an
38 Univariable and
multiple logistic regression analyses were performed, us
39 Multiple logistic regression analyses were used to compa
40 Univariate and
multiple logistic regression analyses were used to evalu
41 Weighted chi2 tests and
multiple logistic regression analyses were used to exami
42 Data were analyzed by linear and
multiple logistic regression analyses, and the Mann-Whit
43 In
multiple logistic regression analyses, independent deter
44 In adjusted
multiple logistic regression analyses, metabolic syndrom
45 Using
multiple logistic regression analyses, shared epitope al
46 ee periods were made by using univariate and
multiple logistic regression analyses.
47 ions disappeared when controlling for sex in
multiple logistic regression analyses.
48 onfidence intervals were determined by using
multiple logistic regression analyses.
49 Multiple logistic-regression analyses showed that DNI wa
50 Data analysis comprised
multiple logistic regression analysis (case-control stud
51 Multiple logistic regression analysis (with generalized
52 R 4.37, 95% CI 1.41-13.54 [P = 0.010]) using
multiple logistic regression analysis adjusting for age,
53 By
multiple logistic regression analysis after adjusting fo
54 Multiple logistic regression analysis assessed the assoc
55 In
multiple logistic regression analysis baseline cardiac i
56 A
multiple logistic regression analysis combining the dose
57 Multiple logistic regression analysis confirmed the cont
58 Multiple logistic regression analysis demonstrated that
59 A new
multiple logistic regression analysis demonstrated the a
60 Multiple logistic regression analysis estimated the inde
61 Multiple logistic regression analysis evaluated the asso
62 ew PBHs or the accumulation of PBHs, while a
multiple logistic regression analysis evaluated the rela
63 Multiple logistic regression analysis for environmental
64 Multiple logistic regression analysis found that factors
65 Multiple logistic regression analysis identified inappro
66 Multiple logistic regression analysis identified non-idi
67 Multiple logistic regression analysis identified underly
68 Factors associated with depression via
multiple logistic regression analysis included younger a
69 Based on
multiple logistic regression analysis of PHS with adjust
70 Multiple logistic regression analysis of the complicatio
71 Multiple logistic regression analysis revealed an increa
72 Multiple logistic regression analysis revealed older age
73 Multiple logistic regression analysis revealed that male
74 Multiple logistic regression analysis revealed that the
75 Multiple logistic regression analysis revealed that, whe
76 Multiple logistic regression analysis showed that female
77 Multiple logistic regression analysis showed that high b
78 Multiple logistic regression analysis showed that increa
79 Multiple logistic regression analysis showed that infect
80 Multiple logistic regression analysis showed that the AL
81 Multiple logistic regression analysis showed that the pa
82 Multiple logistic regression analysis showed that the se
83 Multiple logistic regression analysis showed that visuos
84 Multiple logistic regression analysis showed that, after
85 sex, race/ethnicity, and geographic region;
multiple logistic regression analysis to determine indep
86 y, and vessel volumetry, were used to feed a
multiple logistic regression analysis to find significan
87 Multiple logistic regression analysis utilizing a linear
88 Multiple logistic regression analysis was applied to ide
89 When
multiple logistic regression analysis was applied, this
90 Multiple logistic regression analysis was conducted to d
91 Multiple logistic regression analysis was performed; gen
92 Multiple logistic regression analysis was used to assess
93 Multiple logistic regression analysis was used to determ
94 Multiple logistic regression analysis was used to estima
95 Multiple logistic regression analysis was used to examin
96 Multiple logistic regression analysis was used to explor
97 Stepwise
multiple logistic regression analysis was used to explor
98 Multiple logistic regression analysis was used to invest
99 Multiple logistic regression analysis was used to test f
100 k factors associated with eGFR <60 mL/min in
multiple logistic regression analysis were age (P < 0.00
101 and European QOL 5D Visual Analog Scale via
multiple logistic regression analysis were American regi
102 In a
multiple logistic regression analysis, A1c >7% was a sig
103 We performed
multiple logistic regression analysis, adjusting odds ra
104 After
multiple logistic regression analysis, anti-PF4/heparin
105 In
multiple logistic regression analysis, being overweight
106 ion of the two miRNAs together, tested using
multiple logistic regression analysis, did not improve t
107 In
multiple logistic regression analysis, elderly recipient
108 In
multiple logistic regression analysis, NAFLD was the onl
109 However, in the
multiple logistic regression analysis, only EPE [odds ra
110 In
multiple logistic regression analysis, only PTH increase
111 In
multiple logistic regression analysis, predicted extrava
112 education, and first-trimester exposures in
multiple logistic regression analysis, the authors found
113 In
multiple logistic regression analysis, the prevalence of
114 By
multiple logistic regression analysis, we found that the
115 ce of at least one of the applicable PSIs on
multiple logistic regression analysis, with confirmation
116 sted for a number of possible confounders in
multiple logistic regression analysis.
117 ween periodontal disease and PH on bivariate
multiple logistic regression analysis.
118 Independent factors were identified with
multiple logistic regression analysis.
119 those without CIN by using forward stepwise
multiple logistic regression analysis.
120 versus advanced fibrosis) were explored with
multiple logistic regression analysis.
121 e were independently associated with CDAD by
multiple logistic regression analysis.
122 ndependent of pack-year smoking history with
multiple logistic regression analysis.
123 associated with outcome were entered into a
multiple logistic regression analysis.
124 e only independent predictor of mortality on
multiple logistic regression analysis.
125 ferences in CT features were identified with
multiple logistic regression analysis.
126 dverse events by using Fisher exact test and
multiple logistic regression analysis.
127 aediatric Index of Mortality, and surgeon on
multiple logistic regression analysis.
128 ors for utilization of dental services using
multiple logistic regression analysis.
129 In our
multiple logistic-regression analysis, consumption of ra
130 Multiple logistic-regression analysis, with the use of t
131 Bivariate
multiple logistic regression and adjusted prevalence ana
132 Multiple logistic regression and analysis of covariance
133 Multiple logistic regression and Cox proportional hazard
134 Nonparametric tests as well as
multiple logistic regression and mixed effects logistic
135 Associations were assessed by using
multiple logistic regression and subsequent meta-analysi
136 attributable fractions were derived by using
multiple logistic regression and the Levin formula.
137 Multiple logistic regression and Tobit regression models
138 developing severe renal insufficiency using
multiple logistic regression,
and the predictive ability
139 Multiple logistic regression assessed odds ratio for sur
140 Multiple logistic regression (
c-statistic 0.715, 95% CI:
141 months before interview were obtained using
multiple logistic regression controlling for demographic
142 h out-of-pocket expenses were estimated from
multiple logistic regression controlling for demographic
143 each microorganism with CAL was tested using
multiple logistic regressions controlling for age, smoki
144 28-day survivors, using Bonferroni-corrected
multiple logistic regression,
days alive and free of ven
145 Multiple logistic regression demonstrated a significant
146 Multiple logistic regression demonstrated an increased r
147 Multiple logistic regression demonstrated the following
148 Multiple logistic regressions demonstrated that an open
149 Using the best-fitting
multiple logistic regression equation, a 100-point incre
150 Using
multiple logistic regression,
five features were indepen
151 They used
multiple logistic regression for their comparison.
152 after simultaneously controlling (by use of
multiple logistic regression)
for age, gender and cardia
153 Multiple logistic regression identified having an OC, ag
154 Multiple logistic regression identified independent risk
155 Independent risk factors identified in
multiple logistic regression included chorioamnionitis (
156 rminants of neoatherosclerosis identified by
multiple logistic regression included younger age (p < 0
157 Using
multiple logistic regression,
increased eNO (odds ratio,
158 By
multiple logistic regression,
independent risk factors f
159 At
multiple logistic regression,
kurtosis on T2-weighted im
160 On
multiple logistic regression,
LVOT gradient reduction af
161 ANN and
multiple-logistic-regression (
MLR) models were construct
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 The second approach constructed a
multiple logistic regression model considering significa
166 A
multiple logistic regression model including standard Fr
167 A
multiple logistic regression model incorporating oxygena
168 A
multiple logistic regression model predicting odds of su
169 A
multiple logistic regression model revealed independent
170 A
multiple logistic regression model that included predict
171 found to be significant were entered into a
multiple logistic regression model to identify factors i
172 tooth-level multivariate survival model and
multiple logistic regression model using the method of g
173 A
multiple logistic regression model was estimated at impl
174 A
multiple logistic regression model was then developed to
175 A
multiple logistic regression model was used to evaluate
176 A
multiple logistic regression model was used to identify
177 Two types of a
multiple logistic regression model were fit: 1) logistic
178 Adjusted odds ratios (ORs) from a
multiple logistic regression model were used to estimate
179 In a
multiple logistic regression model with patients aged 65
180 s with bivariate analyses and constructing a
multiple logistic regression model with the number of po
181 s examined using a univariate analysis and a
multiple logistic regression model, adjusting for age, s
182 In a
multiple logistic regression model, African American rac
183 pregnancy physical activity, and income in a
multiple logistic regression model, regular use of multi
184 In a
multiple logistic regression model, risks of incident hy
185 In a
multiple logistic regression model, the G allele was ass
186 In a
multiple logistic regression model, the OR for HLA-B*27:
187 r adjustment for significant covariates in a
multiple logistic regression model, the use of OSP was a
188 In a
multiple logistic regression model, there was a signific
189 ingle regressor analysis were entered into a
multiple logistic regression model.
190 iate predictors of death were entered into a
multiple logistic regression model.
191 analyzed thereafter in a backward selection
multiple logistic regression model.
192 Using a two-stage
multiple-logistic regression model, we found association
193 Multiple logistic regression modeling and propensity sco
194 Multiple logistic regression modeling quantified the ass
195 Multiple logistic regression modeling showed the effect
196 Multiple logistic regression modeling was used to identi
197 Bivariate analyses and
multiple logistic regression modeling were performed.
198 In rejection episode analyses,
multiple logistic regression modelling showed that chang
199 Multiple logistic regression models adjusted for subject
200 Multiple logistic regression models analyzed all variabl
201 Multiple logistic regression models for case-control dat
202 , lifestyle, and sociodemographic factors in
multiple logistic regression models for prediction of th
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 Multiple logistic regression models showed that children
206 smoke exposure with ADHD was examined by two
multiple logistic regression models that differ in the s
207 We used
multiple logistic regression models to adjust for age, s
208 We used
multiple logistic regression models to examine the effec
209 Adjusted
multiple logistic regression models were applied to asse
210 Multiple logistic regression models were developed to ex
211 When
multiple logistic regression models were fit with adjust
212 Multiple logistic regression models were fitted to calcu
213 vidually assessed at the genotype level, and
multiple logistic regression models were used to adjust
214 patient characteristics was determined, and
multiple logistic regression models were used to adjust
215 Generalized linear regression and
multiple logistic regression models were used to assess
216 Multiple logistic regression models were used to assess
217 nquired about "self-assessed periodontitis."
Multiple logistic regression models were used to constru
218 Multiple logistic regression models were used to examine
219 Three
multiple logistic regression models were used to generat
220 Multiple logistic regression models were used to identif
221 CI: 0.88, 1.02 (P = 0.16), respectively] in
multiple logistic regression models with adjustment for
222 ompliance were then evaluated in a series of
multiple logistic regression models with adjustment for
223 Data were analyzed using
multiple logistic regression models with backward stepwi
224 In
multiple logistic regression models, both treatment and
225 rvals (CIs) were obtained from unconditional
multiple logistic regression models, including terms for
226 In the
multiple logistic regression models, the median glycemic
227 Multiple logistic regression models, with tooth-level bl
228 Risk factors were examined in
multiple logistic regression models.
229 laining 70% of the variance were included in
multiple logistic regression models.
230 e adjusted for patient characteristics using
multiple logistic regression models.
231 from 2002-2008 were examined in detail using
multiple logistic regression (
n = 774,399).
232 In
multiple logistic regression,
odds of oral HPV infection
233 association was independently significant in
multiple logistic regression (
P = 0.04) along with race,
234 Multiple logistic regression revealed that females were
235 Multiple logistic regression revealed that the CYP11B2 -
236 Multiple logistic regressions revealed that both Fcgamma
237 Multiple logistic regression showed independent associat
238 Weighted
multiple logistic regressions showed that this relations
239 After adjustment for covariates using
multiple logistic regression,
significantly more African
240 redictive than another using ROC curves, but
multiple logistic regression suggested salT was more pre
241 re compared between cases and controls using
multiple logistic regression techniques.
242 Student t test, chi(2), and
multiple logistic regression tests were performed as app
243 By
multiple logistic regressions,
the following association
244 al abnormalities and asbestos exposure using
multiple logistic regression to adjust for year of birth
245 We used
multiple logistic regression to assess differences in op
246 We used
multiple logistic regression to assess relationships bet
247 We used
multiple logistic regression to assess the association b
248 The authors used
multiple logistic regression to assess the relation betw
249 We used
multiple logistic regression to determine the independen
250 We used
multiple logistic regression to estimate associations be
251 We used
multiple logistic regression to estimate odds ratios (OR
252 We used
multiple logistic regression to estimate predictive marg
253 We used univariate and
multiple logistic regression to examine clinical and lab
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 When
multiple logistic regression was applied to the data, th
261 Multiple logistic regression was performed to analyze th
262 Multiple logistic regression was performed to compare di
263 Multiple logistic regression was performed to study the
264 Multiple logistic regression was performed using a discr
265 pation by self-administration of the survey,
multiple logistic regression was performed.
266 ndent risk factors for hospital mortality by
multiple logistic regression was rupture (P<0.0009), and
267 Multiple logistic regression was used to assess the effe
268 Multiple logistic regression was used to assess the inde
269 Multiple logistic regression was used to assess the use
270 Multiple logistic regression was used to assess the use
271 Multiple logistic regression was used to calculate odds
272 Stepwise
multiple logistic regression was used to calculate the o
273 Multiple logistic regression was used to compare anemia
274 Multiple logistic regression was used to determine signi
275 Multiple logistic regression was used to determine the i
276 Multiple logistic regression was used to determine the r
277 Adjusting for student demographics,
multiple logistic regression was used to determine wheth
278 aseline who continued to drive at follow-up,
multiple logistic regression was used to estimate the od
279 Multiple logistic regression was used to examine associa
280 Multiple logistic regression was used to examine the ind
281 Multiple logistic regression was used to examine the rel
282 A
multiple logistic regression was used to explore the com
283 Multiple logistic regression was used to identify factor
284 Multiple logistic regression was used to identify factor
285 Multiple logistic regression was used to identify indepe
286 A
multiple logistic regression was used to identify indepe
287 Multiple logistic regression was used to identify risk f
288 Multiple logistic regression was used to measure the ass
289 Multiple logistic regression was used to measure the imp
290 Multiple logistic regression was used to quantify the ef
291 Multiple logistic regression was used to test hypotheses
292 Multiple logistic regression was used with mortality as
293 son chi-square tests, Fisher exact test, and
multiple logistic regression,
was performed.
294 Using
multiple logistic regression,
we explored the associatio
295 Using
multiple logistic regression,
we identified significant
296 Random Forests classification and
multiple logistic regression were used to assess the RI
297 Weighted population, prevalence, and
multiple logistic regression were used.
298 Bivariate analysis and
multiple logistic regressions were performed to identify
299 Multiple logistic regressions were used to derive adjust
300 Multiple logistic regression,
with adjustments for demog