1 s to protect soldiers and simplify transport
logistics.
2 slow reporting times, technical demands, and
logistics.
3 nflated Type-I error rates but the Classical
logistic and Bayes logistic (BL) regressions are conserv
4 relating the 52 SNPs to all phenotypes using
logistic and linear regression models.
5 Logistic and linear regression techniques were used to e
6 with AMD sub-phenotypes were analyzed using
logistic and linear regression, and Cox proportional haz
7 analyzed data from 128 capture events using
logistic and ordinal regression to examine risk factors
8 apy (cART) onset were analyzed using linear,
logistic,
and Cox proportional hazard models.
9 r rates but the Classical logistic and Bayes
logistic (
BL) regressions are conservative.
10 uctive pulmonary disease (16.0%), and a mean
logistic EuroSCORE of 18.3+/-13.2.
11 Diagnostic accuracy was evaluated by using
logistic generalized linear mixed-effect models and rece
12 wait list mortality; however, technical and
logistic issues are limiting factors.
13 he incidence assay biomarker dynamics with a
logistic link function assuming individual variabilities
14 mework where optimization of the regularized
logistic loss function is performed with respect to one
15 s work, we propose a dual-network integrated
logistic matrix factorization (DNILMF) algorithm to pred
16 test these various hypotheses we performed a
logistic meta-regression analysis of cure rates from all
17 analyzed under the Bayesian paradigm, using
logistic model and areas under the receiver operating ch
18 porating prior biological knowledge within a
logistic modeling framework by using network-level const
19 Contingency tests and binary
logistic modeling were used to identify baseline predict
20 erent ages with ALS risk using unconditional
logistic models and with survival after ALS diagnosis us
21 Among children with asthma,
logistic models were created to examine the effects of u
22 were well correlated with Verma and modified
Logistic models which gave the best fitting for CD and H
23 We propose a new framework,
Logistic Multiple Network-constrained Regression (LogMiN
24 outcomes and PAM indicators using linear and
logistic multivariate models.
25 ause of a variety of medical, financial, and
logistic obstacles.
26 uss the development of the game, some of the
logistics of game development in this context, and offer
27 morbidity as measured by survivor Pediatric
Logistic Organ Dysfunction score, and biomarkers of endo
28 I, 7.0 +/- 4.6 versus 8.7 +/- 6.4; Pediatric
Logistic Organ Dysfunction, version II, 5.1 +/- 2.2 vers
29 Logistic prediction modelling identified a set of 8 biom
30 els with backward stepwise elimination (Proc
Logistic procedure in SAS).
31 We used a
logistic random-effects model designed to test within-pe
32 se rates by education and income level using
logistic regression (odds ratios).
33 Inverse probability-weighted
logistic regression accounting for age, sex, emergency m
34 We used
logistic regression adjusted by age, sex, and study desi
35 nfidence intervals (CIs) using unconditional
logistic regression adjusted for confounders.
36 matched odds ratios (mORs) using conditional
logistic regression adjusted for maternal age and educat
37 athway abundances and risk using conditional
logistic regression adjusting for BMI, smoking, and alco
38 -month culture conversion using multivariate
logistic regression after adjusting for MIC.
39 We conducted conditional
logistic regression analyses adjusted for body mass inde
40 Logistic regression analyses based on a conceptual model
41 Logistic regression analyses examined the association be
42 Logistic regression analyses examined the number of past
43 Using multivariate
logistic regression analyses four SNPs were significantl
44 Logistic regression analyses revealed a strong/independe
45 Bivariable
logistic regression analyses suggested that high viral l
46 We performed univariate
logistic regression analyses to assess the association b
47 We used conditional
logistic regression analyses to estimate odds ratios for
48 We used
logistic regression analyses to estimate the strength of
49 (univariate and bivariate) and multivariable
logistic regression analyses to longitudinal health insu
50 Multivariable
logistic regression analyses were applied to determine w
51 Multivariable
logistic regression analyses were conducted.
52 Logistic regression analyses were performed to evaluate
53 tion and regression tree (CART) analysis and
logistic regression analyses were performed to identify
54 Univariate tests and
logistic regression analyses were performed, studying th
55 Multivariable
logistic regression analyses were undertaken.
56 Logistic regression analyses were used to identify deter
57 e compared between groups using multivariate
logistic regression analyses, adjusting for maternal age
58 breast cancer were assessed in multivariable
logistic regression analyses.
59 I, -0.29 to 0.14; P = .49) nor by univariate
logistic regression analysis (odds ratio, 0.64; 95% CI,
60 95% CI, 0.22-1.67; P = .37) or multivariate
logistic regression analysis (odds ratio, 1.09; 95% CI,
61 ic and non-atopic children were estimated by
logistic regression analysis adjusting for potential con
62 Univariate and mixed-effects
logistic regression analysis controlling for center effe
63 Logistic regression analysis determined the predictors o
64 analysis for clinical outcome parameter and
logistic regression analysis for postsurgical complicati
65 We used
logistic regression analysis for remission and zero-infl
66 We did a
logistic regression analysis of cannabis use from retros
67 n in any of the LS genes by using polytomous
logistic regression analysis of clinical and germline da
68 Introducing these variables to a
logistic regression analysis showed areas under the rece
69 Logistic regression analysis showed that a decrease in O
70 Multiple
logistic regression analysis showed that female gender,
71 Results from stepwise
logistic regression analysis showed that five biomarkers
72 We performed multinomial
logistic regression analysis to assess the weighting of
73 values of less than 0.1 were considered for
logistic regression analysis to identify predictors of m
74 Multiple
logistic regression analysis was conducted to determine
75 Multivariate
logistic regression analysis was performed for associati
76 Logistic regression analysis was performed to assess pot
77 Multivariable
logistic regression analysis was performed to determine
78 A conditional
logistic regression analysis was performed to evaluate t
79 Multiple
logistic regression analysis was used to estimate adjust
80 Multivariable ordinal
logistic regression analysis with an interaction term wa
81 urgery: risk factors at baseline (univariate
logistic regression analysis) included longer total dura
82 In a multivariable
logistic regression analysis, only moderate to severe br
83 In a
logistic regression analysis, ulcers were identified to
84 Multivariable
logistic regression analysis, using the calculated prope
85 tinuation of therapy were identified using a
logistic regression analysis.
86 ing robotic surgery or CR-POPF occurrence on
logistic regression analysis.
87 readmission risk was evaluated by multilevel
logistic regression analysis.
88 factors associated with 30-day mortality in
logistic regression analysis.
89 r unbalanced covariates, we used conditional
logistic regression and a repeated measures model to com
90 Results of
logistic regression and areas under the curve were compa
91 rimination was similar for the multivariable
logistic regression and CHAID tree models, with both bei
92 nd 95% confidence intervals for asthma using
logistic regression and correcting for the known samplin
93 nd overall survival (OS), was assessed using
logistic regression and Cox models, respectively.
94 Logistic regression and generalized estimating equations
95 were examined using multilevel mixed-effects
logistic regression and multilevel mixed-effects ordinal
96 This analysis demonstrates a simple
logistic regression approach for testing a priori hypoth
97 Although standard
logistic regression approaches were predictive, they wer
98 Multivariate
logistic regression assessed sociodemographic, medical,
99 ntion-to-treat analysis, using multivariable
logistic regression controlling for potential confounder
100 Multivariable
logistic regression examined sociodemographic and clinic
101 Logistic regression explored predictors of depression fr
102 ical analysis was performed with conditional
logistic regression for binary outcomes and the stratifi
103 eviously developed a network-based penalized
logistic regression for correlated methylation data, but
104 Exploratory analysis via binary
logistic regression found a potential association betwee
105 Logistic regression identified that dwell time was the o
106 ctors on >/=2-step DRSS score improvement by
logistic regression in an integrated VISTA and VIVID dat
107 ty selection algorithm with a L1-regularized
logistic regression kernel and were then fitted with log
108 ance with models built with state-of-the-art
logistic regression methods.
109 A multiple
logistic regression model incorporating oxygenation inde
110 A multiple
logistic regression model predicting odds of successful
111 A multivariate
logistic regression model predicting referral to PC was
112 We used a multivariable
logistic regression model to compute the conditional pro
113 cific transcripts, we used a cross-validated
logistic regression model to identify the presence of HC
114 A Bayesian hierarchical
logistic regression model was applied to estimate the no
115 A multivariable
logistic regression model was constructed to quantify th
116 To adjust for selection bias, a
logistic regression model was created to estimate odds r
117 A multiple
logistic regression model was estimated at implant and p
118 A propensity score-weighted
logistic regression model was used to adjust for confoun
119 A hierarchical
logistic regression model was used to identify predictor
120 In a hierarchical
logistic regression model, a routine of early discharge
121 ected mortality was obtained from multilevel
logistic regression model, adjusting for demographics, m
122 Next, we created a
logistic regression model, controlling for comorbidity a
123 In a
logistic regression model, more catatonia signs were ass
124 In fully-adjusted
logistic regression model, the odds ratio (OR) per 10 un
125 prior melanomas was analyzed using an exact
logistic regression model.
126 e to RSV were assessed using a hierarchical,
logistic regression model.
127 Results were analyzed using a
logistic regression model.
128 Multivariable
logistic regression modeling assessed the independent ef
129 test, the t test, the Mann-Whitney test, and
logistic regression modeling of sample adequacy were per
130 Logistic regression modeling was used to examine associa
131 Multivariable
logistic regression modelling was used to identify predi
132 stical methods using 2 independently derived
logistic regression models (a de novo model and an a pri
133 nt amelanotic melanomas were evaluated using
logistic regression models adjusted for age, sex, study
134 For each outcome, we estimated conditional
logistic regression models adjusting for race/ethnicity,
135 Conditional
logistic regression models adjusting for risk factors ev
136 Conditional
logistic regression models adjusting for serum cotinine
137 e extracted and analyzed to fit multivariate
logistic regression models and build a risk calculator.
138 Multivariable
logistic regression models assessed independent associat
139 periodontal severity with linear and ordinal
logistic regression models before and after adjusting fo
140 f the radiosensitive variable improved lasso
logistic regression models compared to model performance
141 We used published data to create
logistic regression models comparing annual trends in th
142 Multivariable
logistic regression models fitted the association of age
143 nd postguideline periods in the hierarchical
logistic regression models for all of the risk groups.
144 Logistic regression models identified characteristics as
145 Multiple
logistic regression models revealed that combining the f
146 In
logistic regression models stratified by race, the media
147 Logistic regression models tested any independent relati
148 METHOD: The authors used
logistic regression models to assess prospective associa
149 regression kernel and were then fitted with
logistic regression models to classify steatosis, that w
150 measures were tested by using multivariable
logistic regression models to determine which combinatio
151 We used
logistic regression models to estimate associations of P
152 METHODS AND We fit mixed-effects
logistic regression models to routine surveillance data
153 We used
logistic regression models under a generalized estimatin
154 Multiple-variable
logistic regression models were built to compare the dia
155 Pooled multivariate
logistic regression models were constructed for each inf
156 Univariate then multivariable
logistic regression models were constructed to assess th
157 Logistic regression models were established for both the
158 Multilevel multivariable
logistic regression models were fitted, adjusting for pa
159 Multivariate linear and
logistic regression models were performed to assess fact
160 Multivariable
logistic regression models were used to assess the exten
161 ts were pooled in case-control analyses, and
logistic regression models were used to compute risks.
162 w-up for >/=3 months, and population average
logistic regression models were used to determine risk f
163 Conditional
logistic regression models were used to estimate odds ra
164 Multivariable unconditional
logistic regression models were used to estimate odds ra
165 Multivariable conditional
logistic regression models were used to estimate odds ra
166 tify potentially confounding covariates, and
logistic regression models were used to estimate the ris
167 Multiple linear and
logistic regression models were used to examine relation
168 s (with 95% confidence interval) and ordinal
logistic regression models were used.
169 rvals (CI) were calculated using conditional
logistic regression models with adjustment for important
170 ces in percent effect changes in conditional
logistic regression models with and without additional a
171 Data were analyzed using multiple
logistic regression models with backward stepwise elimin
172 Logistic regression models with empirical Bayes factors
173 were tested using multivariable hierarchical
logistic regression models, adjusting for important prog
174 We used Cox proportional hazard models,
logistic regression models, and Fine-Gray competing risk
175 In
logistic regression models, cannabis use at wave 1 was a
176 In the multiple
logistic regression models, the median glycemic level wa
177 Using generalised linear and
logistic regression models, we examined the effect of 12
178 tment delay on treatment effectiveness using
logistic regression models.
179 rts via univariate analysis and multivariate
logistic regression models.
180 es and ADHD in offspring were analyzed using
logistic regression models.
181 statin and nonstatin LLT use in hierarchical
logistic regression models.
182 hters at midlife using quantile, linear, and
logistic regression models.
183 Logistic regression or Cox proportional hazard models we
184 Firth's
logistic regression provides a concise statistical infer
185 Spearman's correlations and
logistic regression revealed a general pattern of beech
186 Furthermore,
logistic regression showed that this combination of thes
187 Logistic regression techniques were used to calculate ri
188 Odds ratios were calculated using
logistic regression to account for potential confounders
189 We used mixed effects
logistic regression to analyze the association of diabet
190 We performed
logistic regression to assess correlations between expos
191 We used
logistic regression to assess the strength of associatio
192 adjusted odds ratios with exact conditional
logistic regression to determine the association between
193 We performed a stepwise
logistic regression to develop a multivariate risk predi
194 We used conditional
logistic regression to estimate odds ratios for incident
195 We used multilevel multivariable ordinal
logistic regression to estimate odds ratios.
196 We used
logistic regression to estimate the association between
197 We used multivariate
logistic regression to evaluate the association between
198 We used
logistic regression to examine factors associated with t
199 We used
logistic regression to investigate factors associated wi
200 c information and infection status, and used
logistic regression to relate those covariates to lamb s
201 We used
logistic regression to show the association between visi
202 stages under a fixed-effects model and used
logistic regression to test for association in each stag
203 We used case-only multivariable
logistic regression to test for heterogeneity in associa
204 We used
logistic regression to test the relationship between cha
205 tcome typically entails fitting a polytomous
logistic regression via maximum likelihood estimation.
206 Multivariate
logistic regression was performed and detailed periabsce
207 Multivariable
logistic regression was performed to identify factors as
208 Multivariable
logistic regression was performed to identify factors as
209 In-hospital outcomes were recorded, and
logistic regression was performed to identify independen
210 Mixed-effects
logistic regression was performed to model independent d
211 s (low, moderate, and high) were created and
logistic regression was undertaken to evaluate the optim
212 Multivariable
logistic regression was used to assess associations betw
213 Logistic regression was used to assess risk factors for
214 Logistic regression was used to calculate crude and adju
215 Multivariable
logistic regression was used to calculate odds ratios (O
216 ime trends were identified and multivariable
logistic regression was used to determine sociodemograph
217 Logistic regression was used to determine the independen
218 Binary
logistic regression was used to develop a multivariable
219 Binary multivariable
logistic regression was used to estimate the odds of HPV
220 Logistic regression was used to evaluate the association
221 Logistic regression was used to examine the association
222 Multivariable
logistic regression was used to explore the association
223 n a derivation cohort, and backward stepwise
logistic regression was used to identify factors indepen
224 Logistic regression was used to identify risk factors fo
225 Logistic regression was used to investigate if patient f
226 Logistic regression was used to obtain adjusted odds rat
227 Multivariate
logistic regression was used to predict the outcome.
228 Logistic regression was used to test for an interaction
229 (ORs) and 95% confidence intervals (CIs) by
logistic regression with adjustment for age, gender, and
230 venlafaxine), using multivariable linear and
logistic regression with Bonferroni correction.
231 Logistic regression with generalized estimating equation
232 Multivariable
logistic regression with generalized estimating equation
233 We used multivariate
logistic regression with PCR-confirmed influenza infecti
234 Multivariate
logistic regression with restricted cubic splines was ut
235 e patients versus controls using conditional
logistic regression with results from the 2 settings poo
236 Logistic regression with stepwise variable selection was
237 We used
logistic regression, adjusted for age, sex, race, state
238 h uptake in the non-incentivised group using
logistic regression, adjusting for community and number
239 to predict MDD, using area under the curve,
logistic regression, and linear mixed model analyses, wa
240 In multivariate
logistic regression, cerebral abscess was associated wit
241 In
logistic regression, consistent acute exacerbations (>/=
242 In multivariable
logistic regression, cortical superficial siderosis burd
243 In multivariable
logistic regression, high safe patient handling behavior
244 On multivariate
logistic regression, lower baseline GDF-15 was associate
245 to automatically construct knowledge graphs:
logistic regression, naive Bayes classifier and a Bayesi
246 Using
logistic regression, odds ratios with 95% confidence int
247 On multivariate
logistic regression, only age younger than 50 years, bas
248 After
logistic regression, prior intravitreal injection was as
249 ) at transfer were assessed using linear and
logistic regression, respectively.
250 g mixed effects repeated measures models and
logistic regression, revealed two independent histologic
251 For both analyses, we used
logistic regression, stratified by sex and adjusted for
252 Using multivariable
logistic regression, we assessed correlates of significa
253 With the use of
logistic regression, we characterized the associations o
254 Using multiple
logistic regression, we identified significant associati
255 d Rankin Scale score was analyzed by ordinal
logistic regression, which yields a common odds ratio (O
256 retrospective cohort study using multilevel
logistic regression, with MAC use modeled as a function
257 ptoms was obtained through bivariate ordered
logistic regression.
258 statistics and multivariate random intercept
logistic regression.
259 Frequencies were compared using
logistic regression.
260 ile and RHOA was modeled using multivariable
logistic regression.
261 al HIV transmission for each biomarker using
logistic regression.
262 with second opinion use were evaluated using
logistic regression.
263 experiences were studied using multivariate
logistic regression.
264 ression and multilevel mixed-effects ordinal
logistic regression.
265 zed cutoff points was estimated with ordinal
logistic regression.
266 isk types, and any HPV were calculated using
logistic regression.
267 struction within asthmatics via multivariate
logistic regression.
268 tor common data elements were explored using
logistic regression.
269 modality were identified using multivariable
logistic regression.
270 n with and without CHD by using multivariate
logistic regression.
271 ith the chi(2) test, the Student t test, and
logistic regression.
272 ed care-were assessed by using multivariable
logistic regression.
273 e identified using multivariable conditional
logistic regression.
274 ignancy were evaluated by using multivariate
logistic regression.
275 residual cancer cells) were evaluated using
logistic regression.
276 score </=2 was estimated using multivariable
logistic regression.
277 outcome was investigated with multivariable
logistic regression.
278 dentified trajectory classes was assessed by
logistic regression.
279 ) using Cox proportional hazard analyses and
logistic regression.
280 ) concentrations at place of residence using
logistic regression.
281 ormance of fitted models, was estimated from
logistic regression.
282 s ratios were calculated using multivariable
logistic regression.
283 14 days of LVAD were assessed with stepwise
logistic regression.
284 tors for erosive tooth wear were assessed by
logistic regression.
285 endently associated with PNF on multivariate
logistic regression.
286 odds of cancer of two consecutive scores by
logistic regression.
287 ma or death or until December 31, 2010, with
logistic regression.
288 eographically matched controls by univariate
logistic regression.
289 were then tested with the use of multinomial
logistic regression.An ED, HF, and LFD dietary pattern h
290 Logistic regressions controlled for sociodemographic, cl
291 Multivariate
logistic regressions showed that participants undertakin
292 Descriptive statistics and multivariate
logistic regressions were conducted to evaluate end-of-l
293 Logistic regressions were performed to assess if their i
294 Multivariable
logistic regressions were used to analyze the associatio
295 Logistic regressions were used to determine the associat
296 Conditional fixed-effects
logistic regressions were used to examine predictive rel
297 Generalized estimating equations for
logistic regressions with covariate adjustment were appl
298 rgic disease were estimated with multinomial
logistic regressions.
299 ORs were calculated with the use of Cox and
logistic regressions.The mean +/- SD plasma 25(OH)D conc
300 including consumables, equipment, labor, and
logistics,
which is higher than previously calculated.