1 e after high-volume but not low-volume aSAH (
multivariable logistic regression).
2 p a score to predict 30-day mortality, using
multivariable logistic regression.
3 of propensity score matching and multilevel,
multivariable logistic regression.
4 mes and demographic factors were tested with
multivariable logistic regression.
5 Analyses were conducted using
multivariable logistic regression.
6 ming, and HO-CDI incidence were evaluated by
multivariable logistic regression.
7 om independent risk factors identified using
multivariable logistic regression.
8 ion through two steps of the continuum using
multivariable logistic regression.
9 regression tree analysis and confirmed with
multivariable logistic regression.
10 n Sweden between 1995 and 2015 and performed
multivariable logistic regression.
11 nd temporal associations were examined using
multivariable logistic regression.
12 s ratio 7.83, 95% CI 2.99-23.5; p<0.0001) by
multivariable logistic regression.
13 re calculated with unmatched univariable and
multivariable logistic regression.
14 cause mortality at 90 days was analyzed with
multivariable logistic regression.
15 nt melanoma associated with MC1R variants by
multivariable logistic regression.
16 een timing of SAP and SSI was assessed using
multivariable logistic regression.
17 ariable and risk factors were assessed using
multivariable logistic regression.
18 Predictors of outcomes were identified by
multivariable logistic regression.
19 2008 to 2013 National Inpatient Sample using
multivariable logistic regression.
20 were investigated by applying univariate and
multivariable logistic regression.
21 nd guideline-recommended statin dosing using
multivariable logistic regression.
22 abolic predictors of NWCO were explored with
multivariable logistic regression.
23 rences between study arms were modeled using
multivariable logistic regression.
24 Adjusted odds ratios were calculated using
multivariable logistic regression.
25 rum lipid profile and RHOA was modeled using
multivariable logistic regression.
26 ary treatment modality were identified using
multivariable logistic regression.
27 r (PCP)-directed care-were assessed by using
multivariable logistic regression.
28 ategory (CPC) score </=2 was estimated using
multivariable logistic regression.
29 performed using non-parametric bivariate or
multivariable logistic regression.
30 d compared these with results generated from
multivariable logistic regression.
31 s and in-hospital mortality were assessed in
multivariable logistic regression.
32 ive complications and FTR was evaluated with
multivariable logistic regression.
33 ts/mL and risk factors using univariable and
multivariable logistic regression.
34 LTBI), measured via IGRA, was assessed using
multivariable logistic regression.
35 , including hospitalization and death, using
multivariable logistic regression.
36 ed factors associated with persistent use by
multivariable logistic regression.
37 er using methamphetamine using bivariate and
multivariable logistic regression.
38 spitalization, and conducted univariable and
multivariable logistic regressions.
39 was compared between antibiotic groups with
multivariable logistic regression adjusted for relevant
40 c >7.5% [58 mmol/mol]), were estimated using
multivariable logistic regression adjusted for the same
41 en each exposure and ARDS was determined via
multivariable logistic regression adjusting for potentia
42 We performed a
multivariable logistic regression,
adjusting for age, se
43 We used
multivariable logistic regression,
adjusting for sociode
44 Univariable and
multivariable logistic regression analyses assessed the
45 d descriptive (univariate and bivariate) and
multivariable logistic regression analyses to longitudin
46 Multivariable logistic regression analyses were applied
47 Operation-specific univariate and
multivariable logistic regression analyses were conducte
48 Multivariable logistic regression analyses were conducte
49 Univariable and
multivariable logistic regression analyses were performe
50 Univariable and
multivariable logistic regression analyses were performe
51 Multivariable logistic regression analyses were performe
52 and selection operator (LASSO)-penalized and
multivariable logistic regression analyses were performe
53 Uni- and
multivariable logistic regression analyses were performe
54 Multivariable logistic regression analyses were undertak
55 Multivariable logistic regression analyses were used to
56 Multivariable logistic regression analyses were used.
57 In
multivariable logistic regression analyses, baseline sev
58 outinely universally screened using backward
multivariable logistic regression analyses.
59 rs of 90-day mortality were identified using
multivariable logistic regression analyses.
60 treatment of breast cancer were assessed in
multivariable logistic regression analyses.
61 h PEP was assessed with Univariate tests and
multivariable logistic regression analyses.
62 epwise selection procedure was conducted for
multivariable logistic regression analyses.
63 outinely universally screened using backward
multivariable logistic regression analyses.
64 Multivariable logistic regression analysis identified in
65 Multivariable logistic regression analysis showed an eff
66 Multivariable logistic regression analysis showed that w
67 A
multivariable logistic regression analysis was performed
68 A
multivariable logistic regression analysis was performed
69 Multivariable logistic regression analysis was performed
70 Multivariable logistic regression analysis was performed
71 Multivariable logistic regression analysis was performed
72 Weighted
multivariable logistic regression analysis was then used
73 A
multivariable logistic regression analysis was used to a
74 Univariable and
multivariable logistic regression analysis were used to
75 In a
multivariable logistic regression analysis, an overlap b
76 On
multivariable logistic regression analysis, female sex r
77 In
multivariable logistic regression analysis, higher basel
78 At
multivariable logistic regression analysis, inducible RW
79 In
multivariable logistic regression analysis, MSM were mor
80 In
multivariable logistic regression analysis, obesity was
81 In a
multivariable logistic regression analysis, only moderat
82 In our
multivariable logistic regression analysis, radiation to
83 In
multivariable logistic regression analysis, risk factors
84 In
multivariable logistic regression analysis, the odds of
85 Multivariable logistic regression analysis, using the ca
86 In a
multivariable logistic regression analysis, we investiga
87 Multivariable logistic regression analysis, with synthet
88 cting in-hospital mortality was performed by
multivariable logistic regression analysis.
89 factors associated with undernutrition using
multivariable logistic regression analysis.
90 nsfield unit measurement were assessed using
multivariable logistic regression analysis.
91 et need were determined using bivariable and
multivariable logistic regression analysis.
92 e and laboratory biomarkers were assessed by
multivariable logistic regression analysis.
93 Predictors were evaluated in a
multivariable logistic regression analysis.
94 and cardiovascular factors, and included in
multivariable logistic regression analysis.
95 mia and in-hospital mortality assessed using
multivariable logistic regression analysis.
96 ases) vs rotavirus negative (controls) using
multivariable logistic regression and calculated effecti
97 Discrimination was similar for the
multivariable logistic regression and CHAID tree models,
98 d with overall survival were identified with
multivariable logistic regression and Cox proportional h
99 Multivariable logistic regression and Cox proportional h
100 Multivariable logistic regression and Cox proportional h
101 Multivariable logistic regression and decision curve ana
102 Multivariable logistic regression and Fine and Gray comp
103 tified associations between ESW and AR using
multivariable logistic regression and interval-censored
104 Multivariable logistic regression and multivariable line
105 centile compared with <80th percentile using
multivariable logistic regression and Super Learner with
106 tive variables (P < .05) were entered into a
multivariable logistic regression and tested in an addit
107 Hospital mortality was analyzed using
multivariable logistic regression,
and 1-year mortality
108 rce on trial noncompletion was assessed with
multivariable logistic regression,
and the effect on tim
109 Multivariable logistic regression assessed the impact of
110 From
multivariable logistic regression,
benzodiazepine or Z-d
111 Multivariable logistic regression characterized predicto
112 Multivariable logistic regression controlled for age, se
113 Multivariable logistic regression controlling for demogr
114 modified intention-to-treat analysis, using
multivariable logistic regression controlling for potent
115 use and persistent use was determined using
multivariable logistic regression,
controlling for clini
116 In
multivariable logistic regression,
cortical superficial
117 isk prediction models were constructed using
multivariable logistic regression coupled with receiver
118 Multivariable logistic regression determined factors ind
119 Multivariable logistic regression examined independently
120 Multivariable logistic regression examined sociodemograp
121 In
multivariable logistic regression,
factors associated wi
122 In
multivariable logistic regression,
female sex (adjusted
123 ABSI and culture negative cases respectively.
Multivariable logistic regression for myocardial infarct
124 We performed
multivariable logistic regression for persistent AKI, MA
125 Multivariable logistic regression found corneal profile
126 umes with 90-day mRS score was analysed with
multivariable logistic regression (
functional independen
127 In
multivariable logistic regression,
high safe patient han
128 In
multivariable logistic regression,
hyperferritinemia was
129 Multivariable logistic regression identified 10 neonatal
130 on were found using univariate analyses, and
multivariable logistic regression identified an independ
131 Multivariable logistic regression identified predictors
132 tics with failure to cure was analyzed using
multivariable logistic regression in the total populatio
133 h invasive ventilation) was developed, using
multivariable logistic regression including clinical, ch
134 We modelled each outcome using
multivariable logistic regression,
including stopping br
135 Multivariable logistic regression independently associat
136 Multivariable logistic regression indicated there was no
137 Multivariable logistic regression investigated associati
138 Multivariable logistic regression,
Kaplan-Meier estimate
139 On
multivariable logistic regression,
low posttreatment CA
140 Multivariable logistic regression (
LR) was used to estim
141 nset atrial fibrillation discrimination in a
multivariable logistic regression model (C-statistic 0.8
142 f subclinical atherosclerosis in an adjusted
multivariable logistic regression model (odds ratio, 0.6
143 with a P value of <= .10 were included in a
multivariable logistic regression model adjusting for ag
144 A
multivariable logistic regression model calculated the o
145 We evaluated a
multivariable logistic regression model predicting 5-yea
146 ommendation was the outcome of interest in a
multivariable logistic regression model that included re
147 We used a
multivariable logistic regression model to compute the c
148 , based on model selection, we constructed a
multivariable logistic regression model to predictive PC
149 A
multivariable logistic regression model was constructed
150 A
multivariable logistic regression model was developed in
151 A
multivariable logistic regression model was fit to ident
152 A
multivariable logistic regression model was fitted for e
153 A
multivariable logistic regression model with generalized
154 onal age and mortality was estimated using a
multivariable logistic regression model, adjusting for a
155 In a
multivariable logistic regression model, headache was si
156 In a patient-level
multivariable logistic regression model, only LVOT obstr
157 in-hospital mortality, was analyzed using a
multivariable logistic regression model.
158 e death predictive score were obtained using
multivariable logistic regression model.
159 Multivariable logistic regression modeled death in ICU b
160 Multivariable logistic regression modeling assessed the
161 A weighted
multivariable logistic regression modeling quantified th
162 To determine independent predictors of SD,
multivariable logistic regression modeling was performed
163 Multivariable logistic regression modelling determined a
164 Multivariable logistic regression modelling was used to
165 Multivariable logistic regression models (with spline tr
166 to derive rVE estimates were estimated from
multivariable logistic regression models adjusted for ag
167 ility test results and treatment adequacy in
multivariable logistic regression models adjusting for s
168 Adjusted odds ratios were determined by
multivariable logistic regression models and aggregated
169 Temporal trends were examined by adjusted
multivariable logistic regression models and expressed a
170 level, by developing a series of multilevel
multivariable logistic regression models and geospatiall
171 E and screening analytes were examined using
multivariable logistic regression models containing clin
172 In both
multivariable logistic regression models correcting for
173 Multivariable logistic regression models evaluated the a
174 Multivariable logistic regression models examined associ
175 care were compared by transition status, and
multivariable logistic regression models examined factor
176 Multivariable logistic regression models fitted the asso
177 Newly derived
multivariable logistic regression models for 5-year surv
178 We then developed two separate
multivariable logistic regression models for the outcome
179 Treatment group-adjusted univariable and
multivariable logistic regression models related improve
180 Multivariable logistic regression models tested associat
181 ission and other characteristics to estimate
multivariable logistic regression models to assess the a
182 We estimated
multivariable logistic regression models to assess the r
183 We used mixed-effect
multivariable logistic regression models to identify det
184 Individual univariable and
multivariable logistic regression models were assessed f
185 Hierarchical
multivariable logistic regression models were constructe
186 Univariate then
multivariable logistic regression models were constructe
187 Multivariable logistic regression models were created an
188 Multivariable logistic regression models were created; a
189 Multivariable logistic regression models were fit to com
190 Multilevel
multivariable logistic regression models were fitted, ad
191 Three separate
multivariable logistic regression models were generated:
192 Multivariable logistic regression models were stratified
193 Multivariable logistic regression models were used to as
194 Univariable and
multivariable logistic regression models were used to as
195 Multivariable logistic regression models were used to co
196 Multivariable logistic regression models were used to es
197 Descriptive statistics and
multivariable logistic regression models were used to in
198 Multivariable logistic regression models were used to pr
199 We used
multivariable logistic regression models with interactio
200 We used
multivariable logistic regression models with medical sc
201 We constructed
multivariable logistic regression models, adjusting for
202 Using
multivariable logistic regression models, we identified
203 We used
multivariable logistic regression models, with generaliz
204 cal outcomes were established using adjusted
multivariable logistic regression models.
205 stics between the groups were compared using
multivariable logistic regression models.
206 cement, and hemoptysis was assessed by using
multivariable logistic regression models.
207 rival mode and in-hospital outcomes by using
multivariable logistic regression models.
208 Associations were estimated using
multivariable logistic regression models.
209 at least one prior DBT examination by using
multivariable logistic regression models.
210 We used
multivariable logistic-regression models to assess the a
211 postimplementation VS were determined using
multivariable logistic regression;
participant demograph
212 After adjustment using
multivariable logistic regression,
patients in the high-
213 On
multivariable logistic regression,
patients who received
214 onducted separate analyses using traditional
multivariable logistic regression,
propensity score matc
215 sing three different statistical approaches:
multivariable logistic regression,
propensity score matc
216 titution were incorporated into models using
multivariable logistic regression,
random forests, and a
217 Multivariable logistic regression revealed a statistical
218 In the ECMO cohort,
multivariable logistic regression revealed baseline crea
219 In the ECMO cohort,
multivariable logistic regression revealed baseline crea
220 Multivariable logistic regression revealed peak AST (OR,
221 Multivariable logistic regression reveals that compared
222 Multivariable logistic regression showed that fluid over
223 Multivariable logistic regression showed that pathogenic
224 (T1) and lung procurement was determined by
multivariable logistic regression stratified by propensi
225 " birth cohort (n = 8,327), we used adjusted
multivariable logistic regression to assess the associat
226 nd the primary outcomes using random effects
multivariable logistic regression to control for confoun
227 the gender distribution of authors, and used
multivariable logistic regression to determine character
228 We used univariate and
multivariable logistic regression to determine the demog
229 We used
multivariable logistic regression to estimate VE, adjust
230 In this secondary analysis, we used
multivariable logistic regression to evaluate the associ
231 We performed univariable and
multivariable logistic regression to evaluate the associ
232 In this secondary analysis, we used
multivariable logistic regression to evaluate the associ
233 We conducted
multivariable logistic regression to examine association
234 We used
multivariable logistic regression to examine association
235 We used
multivariable logistic regression to examine association
236 We conducted
multivariable logistic regression to examine the associa
237 rter-year trends for AMA discharges and used
multivariable logistic regression to identify factors as
238 In the intervention group, we used
multivariable logistic regression to identify patient an
239 We used clustered
multivariable logistic regression to identify patient- a
240 We used
multivariable logistic regression to identify patient-an
241 inal, cardiac, chest, or orthopedic and used
multivariable logistic regression to model 30-, 90-, and
242 We used
multivariable logistic regression to model the probabili
243 We used case-only
multivariable logistic regression to test for heterogene
244 A
multivariable logistic regression (
using the propensity
245 Interreader agreement was assessed, and
multivariable logistic regression was also used.
246 Multivariable logistic regression was most effective at
247 Multivariable logistic regression was performed to asses
248 Multivariable logistic regression was performed to ident
249 Multivariable logistic regression was performed to ident
250 Multivariable logistic regression was performed to ident
251 Univariable and
multivariable logistic regression was performed to ident
252 Multivariable logistic regression was performed with nor
253 Multivariable logistic regression was used to analyze pr
254 Multivariable logistic regression was used to assess ass
255 Multivariable Logistic regression was used to assess whe
256 Multivariable logistic regression was used to build pred
257 Multivariable logistic regression was used to calculate
258 Multivariable logistic regression was used to compare ou
259 Multivariable logistic regression was used to compare ou
260 Multivariable logistic regression was used to derive adj
261 nds were calculated for rates over time, and
multivariable logistic regression was used to determine
262 Multivariable logistic regression was used to determine
263 Multivariable logistic regression was used to determine
264 Hierarchical
multivariable logistic regression was used to determine
265 Multivariable logistic regression was used to determine
266 Time trends were identified and
multivariable logistic regression was used to determine
267 Multivariable logistic regression was used to determine
268 Multivariable logistic regression was used to develop a
269 Multivariable logistic regression was used to develop th
270 Multivariable logistic regression was used to estimate O
271 Multivariable logistic regression was used to estimate t
272 Binary
multivariable logistic regression was used to estimate t
273 Multivariable logistic regression was used to evaluate a
274 Multivariable logistic regression was used to evaluate f
275 Multivariable logistic regression was used to evaluate k
276 Multivariable logistic regression was used to evaluate t
277 Multivariable logistic regression was used to evaluate t
278 djusted and probability-weighted multinomial
multivariable logistic regression was used to examine as
279 Multivariable logistic regression was used to explore th
280 Multivariable logistic regression was used to identify f
281 Multivariable logistic regression was used to identify i
282 Multivariable logistic regression was used to identify p
283 Multivariable logistic regression was used to identify p
284 Multivariable logistic regression was used to identify p
285 Multivariable logistic regression was used to identify r
286 Multivariable logistic regression was used to investigat
287 Multivariable logistic regression was used to investigat
288 Multivariable logistic regression was used to study whet
289 Multivariable logistic regression was utilized to assess
290 Using
multivariable logistic regression,
we assessed correlate
291 Using
multivariable logistic regression,
we examined factors a
292 Risk factors identified by
multivariable logistic regression were donor age (P = 0.
293 The Fisher exact test and
multivariable logistic regression were used to evaluate
294 Multivariable logistic regressions were used to analyze
295 Multivariable logistic regressions were used to identify
296 years) and benign breast disease (BBD) using
multivariable logistic regression with generalized estim
297 Multivariable logistic regression with generalized estim
298 Multivariable logistic regression with predictive margin
299 ntile, uncomplicated cases) was modeled with
multivariable logistic regression with robust standard e
300 ios (ORs) with 95% CIs using univariable and
multivariable logistic regression,
with sex, age, smokin