1 tanding tumor biology and planning treatment
logistics.
2 ressed as adjusted mean, in days) as well as
logistic (
adjusted proportion of LoS > 4 days vs. LoS <=
3 Multivariate
logistic analysis revealed that the significant risk fac
4 Multivariable
logistic and Cox proportional hazards regression were ap
5 ls of tumor growth included the exponential,
logistic and Gompertz models.
6 Multivariate
logistic and linear regression models were used to estim
7 sted temporal trends were investigated using
logistic and linear regression.
8 The authors also discuss
logistics and curricular design with common core element
9 Stigma,
logistics,
and ethical factors constrain ECT administrat
10 wer flights and longer wait times can impact
logistics as well as cold ischemia time; our findings mo
11 generated calibration curves (five-parameter
logistic fit p < 0.05) by plotting the measured ratios o
12 of human mobility, production linkages, and
logistics for regional management and planning.
13 r interaction between cells, via a nonlinear
logistic growth model, and our approximations capture th
14 Cross-validated
logistic lasso models were used to estimate the predicte
15 and benign lesions were assessed by using a
logistic mixed model.
16 A longitudinal
logistic mixed-effects model was used to model daily abs
17 bles (not growth phase) for inclusion in the
logistic model and nomogram.
18 with willingness to take MDR TPT, a marginal
logistic model was fitted using generalized estimating e
19 A multivariable
logistic model was used to identify factors mediating 90
20 the occurrence of periodontitis in the final
logistic model were: MetS (odds ratio [OR] = 2.02; P = 0
21 = 66) were analyzed by using a multivariate
logistic model.
22 The exponential and-more notably-
logistic models failed to describe the experimental data
23 In
logistic models, compared with patients receiving neithe
24 ability of disease control, expense, and the
logistics of introducing them into crop production syste
25 alyses with such a risk factor using linear,
logistic,
or Cox proportional hazards models.
26 Cox,
logistic,
or linear statistical models were used dependi
27 included immunocompromised status, Pediatric
Logistic Organ Dysfunction 2 score, day 0 vasopressor-in
28 3) Pediatric Risk of Mortality and Pediatric
Logistic Organ Dysfunction scores at PICU admission were
29 atio [95% CI]), summation of daily Pediatric
Logistic Organ Dysfunction scores, 1.01/per point (1.01-
30 s (CAL) >= 3 mm] were estimated using robust
logistic quantile regression, adjusting for age, sex, et
31 l (0.780), and [Formula: see text] penalized
logistic regression (0.780).
32 For this study, traditional multivariate
logistic regression (LR) identified seven predictors of
33 Multivariable
logistic regression (LR) was used to estimate the risk a
34 Using multinomial
logistic regression (MLR), we compared the 3 panels on t
35 dress the problem: sparse label-noise-robust
logistic regression (Rlogreg), robust elastic net based
36 We used unconditional
logistic regression adjusted for the matching factors to
37 ol/mol]), were estimated using multivariable
logistic regression adjusted for the same hypothesised c
38 Logistic regression analyses adjusting for age, all the
39 Adjusted
logistic regression analyses and generalized estimating
40 GBD) super-regions, with adjusted linear and
logistic regression analyses examining associations with
41 -19 after their stroke, were included in two
logistic regression analyses examining which features we
42 Univariate and multivariate
logistic regression analyses have been performed for bot
43 Logistic regression analyses showed that the clip use di
44 We performed multiple linear and
logistic regression analyses to determine whether HIV/HC
45 escriptive analyses and multivariable binary
logistic regression analyses were conducted on weighted
46 Logistic regression analyses were performed and adjusted
47 Univariable and multivariable
logistic regression analyses were performed to identify
48 Multivariate
logistic regression analyses were undertaken.
49 Multivariable
logistic regression analyses were used.
50 e obtained for the entire lung, and multiple
logistic regression analyses with areas under the curve
51 (descriptive, sequence pattern analyses, and
logistic regression analyses) aimed to detect any combin
52 In multivariable-adjusted conditional
logistic regression analyses, better adherence to the Me
53 ng novel intracountry risk-adjusted UR trend
logistic regression analyses, can be translated to other
54 es, LSI, and FLD were assessed in linear and
logistic regression analyses.
55 le odds ratios (OR) for CAD from conditional
logistic regression analyses.
56 f PnR success at 12 months in a multivariate
logistic regression analysis (P = 0.006).
57 Multiple
logistic regression analysis demonstrated that increased
58 Logistic regression analysis identified cervical spinal
59 Binary
logistic regression analysis proposed a mid-trimester bi
60 However, ordinal
logistic regression analysis revealed that a higher abun
61 Univariate
logistic regression analysis revealed that an adenoma co
62 A
logistic regression analysis showed that the following v
63 Multivariate
logistic regression analysis showed women with hydrosalp
64 umab under local sedation using multivariate
logistic regression analysis to control for potentially
65 Logistic regression analysis was applied to estimate the
66 Weighted multivariable
logistic regression analysis was then used to develop a
67 Logistic regression analysis was undertaken to identify
68 Logistic regression analysis was used to evaluate the di
69 Univariate and multivariate
logistic regression analysis were performed to identify
70 oth a post hoc Bayesian analysis and a mixed
logistic regression analysis were performed.
71 values of less than 0.1 were considered for
logistic regression analysis which identified predictors
72 Using multivariate
logistic regression analysis with leave-1-out cross vali
73 In a multivariable
logistic regression analysis, an overlap between the abl
74 aracteristic analysis, time-series analysis,
logistic regression analysis, and multilevel modeling fo
75 In multivariable
logistic regression analysis, higher baseline IOP predic
76 In the multivariate
logistic regression analysis, individuals living in area
77 In multivariable
logistic regression analysis, risk factors for severe in
78 In a multivariable
logistic regression analysis, we investigated the risk o
79 Multivariable
logistic regression analysis, with synthetic minority ov
80 ratios (ORs) were calculated as part of the
logistic regression analysis.
81 ated cirrhosis and HCC were determined using
logistic regression analysis.
82 nd risk factors for EE were identified using
logistic regression analysis.
83 te AMD patients and control individuals with
logistic regression analysis.
84 We used ordinal
logistic regression and applied generalized estimating e
85 Ordinal
logistic regression and bootstrapped backwards selection
86 Multivariable
logistic regression and Cox proportional hazards models
87 postoperative mortality was evaluated using
logistic regression and Cox proportional hazards models.
88 Multivariate stepwise
logistic regression and Cox proportional-hazard models w
89 We used multiple
logistic regression and difference-in-differences method
90 into development and validation cohorts: the
logistic regression and gradient boosting machine models
91 tions between ESW and AR using multivariable
logistic regression and interval-censored survival analy
92 under the curve for viral RNA shedding using
logistic regression and Kaplan-Meier analyses.
93 Multinomial
logistic regression and linear regression were used to r
94 Binary
logistic regression and multivariate analysis were condu
95 hbor, support vector machine, random forest,
logistic regression and Naive Bayes.
96 To this end, both linear
logistic regression and nonlinear Random Forest classifi
97 Logistic regression and random forests using diagnostic
98 Performance of the ANN was evaluated against
logistic regression and the standard grading system by a
99 patients with active TB were compared using
logistic regression and time-to-event analyses.
100 expression prediction methods and two novel
logistic regression approaches across five GTEx v8 tissu
101 Odds ratios were estimated using conditional
logistic regression by comparing the occurrence of switc
102 The
logistic regression coefficients were identical between
103 Multivariable
logistic regression controlling for demographic and clin
104 Multivariable
logistic regression found corneal profile and IOL type t
105 s, of which 38 loci would be missed within a
logistic regression framework with a binary phenotype de
106 Logistic regression incorporating respondent-driven samp
107 tic curve (0.78 [95% CI 0.77-0.78]) than the
logistic regression model (0.73 [0.72-0.74]) (p < 0.001)
108 brillation discrimination in a multivariable
logistic regression model (C-statistic 0.82 vs 0.78; p =
109 There was no difference in
logistic regression model accuracy comparing the data by
110 A
logistic regression model after the intention-to-treat p
111 A multivariable
logistic regression model calculated the odds ratio (OR)
112 The
logistic regression model combining T2-weighted SI ratio
113 The binary
logistic regression model included the minimal corneal t
114 ccuracy can be achieved using a multivariate
logistic regression model of MRI parameters after thresh
115 A
logistic regression model revealed that CCC was associat
116 A
logistic regression model showed that non-obese patients
117 In a multiple
logistic regression model the factor wound irrigation wi
118 We used
logistic regression model to develop Model 1 by retainin
119 ayesian multivariate response random effects
logistic regression model to simultaneously examine vari
120 A
logistic regression model trained using samples collecte
121 In this study, a
logistic regression model was developed to quantify the
122 Descriptive statistics and a binary
logistic regression model were used to analyze the data.
123 opulation was calculated, and a multivariate
logistic regression model with random intercepts was use
124 ortality was estimated using a multivariable
logistic regression model, adjusting for age, sex, indig
125 In a binary
logistic regression model, young age (P = 0.033), histor
126 ortality, was analyzed using a multivariable
logistic regression model.
127 [WSS]) to identify relevant parameters for a
logistic regression model.
128 ar versus surgical revascularization using a
logistic regression model.
129 fier were 94.2%, 96.9%, 97%, and 94% for the
logistic regression model; 92.7%, 100%, 100%, and 92.9%
130 Multivariable conditional
logistic regression modeling compared the odds of underg
131 onse were assessed using a linear and binary
logistic regression modeling for the continuous and cate
132 Multinomial
logistic regression modeling indicated that Drymarchon c
133 By fitting three multiple
logistic regression models (one for each delivery mode),
134 We computed
logistic regression models adjusted for age, sex, BMI, s
135 We used conditional
logistic regression models adjusted for HDL cholesterol
136 We used interrupted time series
logistic regression models and estimated marginal effect
137 eloping a series of multilevel multivariable
logistic regression models and geospatially visualising
138 nd nephrectomy type (partial/radical)-to fit
logistic regression models and grouped patients accordin
139 Adjusted
logistic regression models and meta-analyses were perfor
140 Logistic regression models and random forest models clas
141 Logistic regression models assessed correlates of non-RT
142 Logistic regression models combining T2-weighted SI and
143 ated multivariate ordinary least squares and
logistic regression models controlling for a wide range
144 In both multivariable
logistic regression models correcting for propensity sco
145 Logistic regression models evaluated the relation of bas
146 ared by transition status, and multivariable
logistic regression models examined factors associated w
147 Multivariable
logistic regression models tested associations of geogra
148 postoperative outcomes, we used multivariate
logistic regression models to adjust for clinical and de
149 an index encounter, and we used multivariate
logistic regression models to assess demographic and cli
150 models, and, in a post-hoc analysis, we used
logistic regression models to assess the association bet
151 Logistic regression models to determine covariate risk c
152 ted AUC for glaucoma versus nonglaucoma from
logistic regression models using MRW-BMO values from all
153 Individual univariable and multivariable
logistic regression models were assessed for each time-w
154 Hierarchical multivariable
logistic regression models were constructed to evaluate
155 Univariate and multivariate
logistic regression models were created.
156 Logistic regression models were developed using lncRNAs
157 Logistic regression models were fitted to determine the
158 Conditional
logistic regression models were used to adjust for poten
159 Logistic regression models were used to analyze risk fac
160 Univariable and multivariable
logistic regression models were used to assess predictor
161 Multivariable Cox and
logistic regression models were used to assess the indep
162 Adjusted path analysis
logistic regression models were used to assess the role
163 Unconditional
logistic regression models were used to calculate lung c
164 Hierarchical
logistic regression models were used to determine associ
165 Unconditional
logistic regression models were used to estimate odds ra
166 Polytomous
logistic regression models were used to estimate ORs and
167 Multivariable
logistic regression models were used to estimate the odd
168 Logistic regression models were used to estimate the odd
169 Logistic regression models were used to evaluate associa
170 Multivariable
logistic regression models were used to provide adjusted
171 Logistic regression models were utilized to estimate the
172 We used multivariable
logistic regression models with medical school-specific
173 ere computed using multivariable conditional
logistic regression models, according to center, sex, ag
174 Using multivariable-adjusted conditional
logistic regression models, caffeic acid (ORlog2: 0.55;
175 tics associated with results reporting using
logistic regression models, described sponsor-level repo
176 In
logistic regression models, the likelihood that RGS was
177 ion criterion in a stepwise fashion to build
logistic regression models, which were then translated i
178 the groups were compared using multivariable
logistic regression models.
179 ith in-hospital mortality was analyzed using
logistic regression models.
180 eralized estimation equations and multilevel
logistic regression models.
181 a infection and symptomatic infection we use
logistic regression models.
182 diagnosis were calculated using multivariate
logistic regression models.
183 risk factors with incident PD using adjusted
logistic regression models.
184 ociations were estimated using multivariable
logistic regression models.
185 efore age 1 were compared between groups via
logistic regression models.
186 ession and predicted response to ICS through
logistic regression models.Measurements and Main Results
187 Conditional
logistic regression odds ratios (ORs) accounting for ind
188 Multivariate
logistic regression of the retrospective cohort demonstr
189 Multivariate
logistic regression selected combined serum alpha-fetopr
190 Stepwise
logistic regression selected four features from PSPR as
191 Logistic regression showed increasing odds of respirator
192 Multiple
logistic regression showed that all algorithm parameters
193 n: Despite the heterogeneous patient cohort,
logistic regression TCP models showed a strong associati
194 (n = 8,327), we used adjusted multivariable
logistic regression to assess the associations of each c
195 We used multivariable conditional
logistic regression to calculate odds ratios (ORs).
196 outcomes using random effects multivariable
logistic regression to control for confounding.
197 We used mixed-effects
logistic regression to estimate associations between eac
198 ffects models to estimate tree densities and
logistic regression to estimate mortality by size class.
199 ied case-crossover analysis with conditional
logistic regression to estimate the association between
200 We used conditional
logistic regression to estimate unadjusted and multivari
201 We used multivariable
logistic regression to examine associations between co-r
202 We used
logistic regression to examine likelihood of second fill
203 time trends during the study period and used
logistic regression to examine sociodemographic and clin
204 We used multinomial
logistic regression to generate covariates of care and v
205 We also used multinomial
logistic regression to identify factors associated with
206 We used multivariable ordinal
logistic regression to identify factors associated with
207 he intervention group, we used multivariable
logistic regression to identify patient and medication c
208 chest, or orthopedic and used multivariable
logistic regression to model 30-, 90-, and 180-day posto
209 multiple imputation for missing covariates,
logistic regression to model the association between PFA
210 ation or marriage), and first birth and used
logistic regression to show the change in prevalence of
211 Multivariate
logistic regression was performed controlling for factor
212 Multiple
logistic regression was performed on demographic and ana
213 Hierarchical
logistic regression was performed to account for cluster
214 Logistic regression was performed to assess associations
215 Logistic regression was performed to assess the predicti
216 Multivariate
logistic regression was performed to define risk factors
217 Logistic regression was performed to determine the signi
218 Univariable and multivariable
logistic regression was performed to identify TO predict
219 Stepwise
logistic regression was performed to select the optimal
220 association between MetS components and DII
Logistic regression was used (P > 0.05).
221 Logistic regression was used to assess ORs with 95% CIs.
222 Survey-adjusted
logistic regression was used to compare the odds for in-
223 Logistic regression was used to compare the odds of preg
224 Conditional
logistic regression was used to create models of associa
225 Multinomial
logistic regression was used to estimate ORs and 95% CIs
226 Logistic regression was used to evaluate the relationshi
227 Multilevel mixed effects
logistic regression was used to examine relationships fo
228 Multivariable
logistic regression was used to identify factors associa
229 Multivariate
logistic regression was used to identify factors predict
230 Multivariable
logistic regression was used to identify risk factors fo
231 Multivariable
logistic regression was utilized to assess the associati
232 Backward selection and multivariate
logistic regression were conducted to assess risk of GI
233 Single factor analysis and
logistic regression were performed, and a composite risk
234 escriptive statistical analysis and multiple
logistic regression were performed.
235 Univariate and multivariate
logistic regression were used to characterize factors as
236 The Fisher exact test and multivariable
logistic regression were used to evaluate association of
237 r combinations to predict GGG 1 vs >1, using
logistic regression with a nested leave-pair out cross v
238 h outcomes were determined using conditional
logistic regression within surveys, adjusting for prespe
239 Logistic regression yielded adjusted odds ratios (ORs) p
240 pendently, (ROC analysis, followed by binary
logistic regression) only Ultrasound depth is a signific
241 C groups and risk factors for fatal outcome (
logistic regression) were evaluated.
242 olume but not low-volume aSAH (multivariable
logistic regression).
243 In binary
logistic regression, a cICA-PO was independently associa
244 er first-ever intracerebral hemorrhage using
logistic regression, adjusting for known predictors of o
245 by ancestry was assessed using multivariate
logistic regression, adjusting for parity, and maternal
246 btype specific risks were estimated by using
logistic regression, and absolute risks were calculated.
247 tion, and delayed graft function (DGF) using
logistic regression, and length of stay (LOS) using nega
248 Using
logistic regression, higher fruit and high vegetable den
249 were estimated using mixed-effects linear or
logistic regression, including a random effect to adjust
250 Using
logistic regression, our study further demonstrated that
251 After adjustment using multivariable
logistic regression, patients in the high-risk group wer
252 ity of illness and should be dosed "enough,"
logistic regression, propensity score matching, and inve
253 ate analyses using traditional multivariable
logistic regression, propensity score matching, propensi
254 Logistic regression, random forest, and support vector m
255 redictive algorithms were developed based on
logistic regression, random forests, gradient boosted tr
256 54 algorithms, the best performing model was
logistic regression, using 1000 features, 100 stop words
257 set of independent variables was selected by
logistic regression, using the derivation set to create
258 sing descriptive statistics and multivariate
logistic regression, we examined the association (P < .0
259 te, a case-crossover design, and conditional
logistic regression, we examined the association between
260 ectiveness (VE) was estimated by conditional
logistic regression, with adjustment for reported contac
261 Leveraging
logistic regression-, random forest- and gradient boosti
262 c associations with multivariable polytomous
logistic regression.
263 study arms were modeled using multivariable
logistic regression.
264 equency (%) was compared using unconditional
logistic regression.
265 tors of CICU mortality were identified using
logistic regression.
266 kness loss were identified with multivariate
logistic regression.
267 through univariate analysis and multivariate
logistic regression.
268 ng non-parametric bivariate or multivariable
logistic regression.
269 Conventional approaches used
logistic regression.
270 for confounders, were estimated by means of
logistic regression.
271 vel between 1-1,000 U/ml was estimated using
logistic regression.
272 ng linear regression and with glaucoma using
logistic regression.
273 d adjusted risk differences (ARDs) following
logistic regression.
274 se with results generated from multivariable
logistic regression.
275 sion (<1000 copies per mL) at 6 months using
logistic regression.
276 ons and FTR was evaluated with multivariable
logistic regression.
277 R, which absorbs the merits of both SCCA and
logistic regression.
278 s associated with eGFR <90 mL/min/1.73 m2 by
logistic regression.
279 of women using contraception with fractional
logistic regression.
280 ment and severity was examined using ordinal
logistic regression.
281 ons and mortality were assessed using binary
logistic regression.
282 tal mortality were assessed in multivariable
logistic regression.
283 tors associated with PIR were assessed using
logistic regression.
284 able predictors of cPR were identified using
logistic regression.
285 mphetamine using bivariate and multivariable
logistic regression.
286 score matching and multilevel, multivariable
logistic regression.
287 iminate between phenotypes was assessed with
logistic regression.
288 idence intervals (CIs) were determined using
logistic regression.
289 redicting mortality using backwards stepwise
logistic regression.
290 rotective titres were estimated using scaled-
logistic-
regression to model pre-transmission titre agai
291 Individual
logistic regressions were performed for 12-month mortali
292 Univariate and multivariate
logistic regressions were performed, and population attr
293 evaluated using network analysis; linear and
logistic regressions were used to compare groups based o
294 Descriptive statistics and multivariate
logistic regressions were used to examine associations b
295 as appropriate) and multivariable analyses (
logistic regressions).
296 In the multiple
logistic regressions, BMI >=27.0 kg/m(2) , WC >=90.0 cm
297 d interactions were determined by linear and
logistic regressions.
298 d marbling values were verified by linear or
logistic regressions.
299 t, were determined using univariate Bayesian
logistic regressions.
300 Four NTCP models, Lyman,
Logistic,
Weibull and Poisson, were fit to the populatio