1 (r = 0.562; P-Value = 0.01, forward stepwise
regression analysis).
2 l testing as appropriate, with multivariable
regression analysis.
3 e used as covariates in the multivariate Cox
regression analysis.
4 value of CTOI was assessed through logistic
regression analysis.
5 ide chains using accurate competitive linear
regression analysis.
6 gnificant predictors of effectiveness in the
regression analysis.
7 d 1 year using multivariate Cox proportional
regression analysis.
8 n period, and analyzed with segmented linear
regression analysis.
9 BV and CAL was revealed by multiple logistic
regression analysis.
10 tality assessed using multivariable logistic
regression analysis.
11 erforming test was determined using logistic
regression analysis.
12 tients and control individuals with logistic
regression analysis.
13 on and prognosis for breast cancer using Cox
regression analysis.
14 taset of 10,000 photographs and phylogenetic
regression analysis.
15 revention as baseline risk increases: a meta-
regression analysis.
16 s) were calculated and adjusted via logistic
regression analysis.
17 al gonorrhea incidence as the determinant in
regression analysis.
18 ed with clinical metadata using multivariate
regression analysis.
19 ost-LT outcomes were evaluated with logistic
regression analysis.
20 y multinomial regression and multiple linear
regression analysis.
21 om age-dependent genes, identified by robust
regression analysis.
22 cluded ANOVA, Wilcoxon test and multivariate
regression analysis.
23 l survival (OS) evaluated using adjusted Cox
regression analysis.
24 lity was performed by multivariable logistic
regression analysis.
25 toma volume, and timing of surgery with meta-
regression analysis.
26 ciations were determined using multivariable
regression analysis.
27 b were imputed as non-responders in logistic
regression analysis.
28 undernutrition using multivariable logistic
regression analysis.
29 MPs and DMRs) were identified through linear
regression analysis.
30 gitudinal change in clinical variables using
regression analysis.
31 plan-Meier analysis, log-rank tests, and Cox
regression analysis.
32 V or macular atrophy were investigated using
regression analysis.
33 th/transplantation) were assessed, using Cox
regression analysis.
34 estic product (GDP) with a multiple logistic
regression analysis.
35 relation, bivariate regression, and multiple
regression analysis.
36 Multivariable analysis included partial
regression analysis.
37 azard ratios (HRs) were calculated using Cox
regression analysis.
38 e rate ratios obtained in log-linear Poisson
regression analysis.
39 e assessed based on sex and age groups, with
regression analysis.
40 n was statistically evaluated using logistic
regression analysis.
41 sociated with cardiac damage by multivariate
regression analysis.
42 guidance were compared by using univariable
regression analysis.
43 with RA estimated using iterative two-phase
regression analysis.
44 , we used single breakpoint linear segmented
regression analysis.
45 long with independent risk factors using Cox-
regression analysis.
46 ORs) were calculated as part of the logistic
regression analysis.
47 actors for EE were identified using logistic
regression analysis.
48 hosis and HCC were determined using logistic
regression analysis.
49 st and multivariate Cox proportional-hazards
regression analysis.
50 tors, and included in multivariable logistic
regression analysis.
51 With multiple
regression analysis,
a forward selection procedure was u
52 In multivariate Cox
regression analysis,
absence of postoperative smoking {o
53 In multivariable Cox
regression analysis accounting for established prognosti
54 Cox
regression analysis adjusted for patients' age, sex, pse
55 Cause-specific Cox
regression analysis adjusted for stroke, head trauma, al
56 A multivariate
regression analysis,
adjusted for age and sex, was emplo
57 Multivariate linear
regression analysis,
adjusted for covariates, indicated
58 sessed using Kaplan-Meir methodology and Cox
regression analysis adjusting for demographics, comorbid
59 Regression analysis after adjusting for likely confounde
60 On multivariate logistic
regression analysis,
after adjusting for age, gender, tr
61 After multivariable Cox
regression analysis,
age >70 (HR, 4.16; 95% CI, 1.78-9.7
62 DNN, called the DNN score, was included in a
regression analysis alongside age, gender, race/ethnicit
63 In a multivariable logistic
regression analysis,
an overlap between the ablation les
64 ative Observational Study, a weighted linear
regression analysis and a novel penalized spline-based s
65 Measurements were compared by linear
regression analysis and Bland-Altmann Plots, using radio
66 nd QLQ-OG25 were identified by multivariable
regression analysis and combined to form a tool.
67 d association of frailty with outcomes using
regression analysis and compared true positive and false
68 First, using voxel-wise multiple
regression analysis and controlling for CSF biomarkers,
69 Linear mixed model
regression analysis and generalized estimating equations
70 Cox
regression analysis and Kaplan-Meier curves were used fo
71 using time-dependent Cox proportional hazard
regression analysis and landmark analysis.
72 Multivariate Cox
regression analysis and propensity score matching were d
73 Regression analysis and Random Forest models were perfor
74 Cross-trait linkage disequilibrium score
regression analysis and trait-relevant tissue analysis s
75 factors for LEE were evaluated with logistic
regression analysis,
and critical P values were addition
76 tic analysis, time-series analysis, logistic
regression analysis,
and multilevel modeling for repeate
77 Correlation statistics, linear
regression analysis,
and tests of means were applied usi
78 l (OS) was evaluated using multivariable Cox
regression analysis,
before and after propensity-score m
79 In the Cox
regression analysis,
bladder drained pancreas was associ
80 In linear
regression analysis,
BMI was positively associated with
81 IR) detection and subsequent global spectral
regression analysis can resolve structural and thermodyn
82 Random effects
regression analysis confirmed decision-making predicts V
83 The stepwise
regression analysis contributed to a metabolomics score
84 In a Cox
regression analysis controlling for age, gender, and a d
85 In the multivariable
regression analysis controlling for confounders, uncemen
86 Cox
regression analysis controlling for potential confounder
87 d units (HU) were analysed and multivariable
regression analysis controlling for traditional cardiova
88 Sample weighted
regression analysis,
controlling for creatinine, sex, ag
89 In the adjusted competing risk
regression analysis,
CysC >= 1.5 mg/L, sarcopenia and ME
90 The time-dependent Cox
regression analysis demonstrated a higher mortality in t
91 Multivariate
regression analysis demonstrated an independent reductio
92 Regression analysis demonstrated no association between
93 Linear
regression analysis demonstrated significant correlation
94 Regression analysis demonstrated that ABR patients had g
95 Moreover, univariate
regression analysis demonstrated that hs-CRP (P <.001) a
96 Multiple logistic
regression analysis demonstrated that increased inferior
97 A logistic
regression analysis demonstrated that these nine genes a
98 Multivariate logistic
regression analysis demonstrated the progression was sig
99 A
regression analysis determined if individual factor scor
100 In
regression analysis,
DFAalpha1 and MSE scale 5 remained
101 In multiple linear
regression analysis,
diabetes (coefficient, 10.1; 95% CI
102 A cox-
regression analysis for post-liver transplant HCC recurr
103 In the multiple linear
regression analysis,
for each increase in the consumptio
104 age category, in an interrupted time series
regression analysis,
for the periods of 1991-1995 (preva
105 as excellent or not excellent, multivariate
regression analysis found several factors to be signific
106 Multivariate
regression analysis from OP daily measurements suggested
107 In a multiple
regression analysis,
GDF-15 (growth and differentiation
108 By multivariable Cox
regression analysis,
GLS remained independently associat
109 en CT and MRI guidance, with univariable Cox
regression analysis hazard ratios of 0.97 (95% CI: 0.57,
110 h better graft outcome in a multivariate Cox
regression analysis (
hazard ratio, 0.260; 95% CI, 0.104-
111 th all-cause mortality in the univariate Cox
regression analysis (
hazard ratio, 1.09 [95% CI, 1.05-1.
112 uL outcome for deletion allele carriers (Cox
regression analysis:
hazard ratio, 2.4 [95% confidence i
113 In a logistic
regression analysis,
high HCoV genomic loads (cycle thre
114 In multivariate
regression analysis,
high-density lung volume was identi
115 In multivariable logistic
regression analysis,
higher baseline IOP predicted highe
116 A lasso-based model and multivariate cox
regression analysis identified a chromosome 17p loss, co
117 A Cox
regression analysis identified age and diagnoses other t
118 Logistic
regression analysis identified cervical spinal cord GM C
119 Multivariable logistic
regression analysis identified initial nonshockable card
120 LASSO-penalized logistic
regression analysis identified substantial necrosis, hig
121 aytime sleepiness score, we applied logistic
regression analysis in crude and adjusted models.
122 In
regression analysis,
in-hospital mortality was associate
123 Multiple linear
regression analysis incorporating WLenh and series 1 DAe
124 In logistic
regression analysis,
independent factors associated with
125 In the multivariate
regression analysis,
independent predictors of CAV were:
126 Multilevel
regression analysis indicated that clinical improvement
127 Regression analysis indicated that impact survey TF1-9 p
128 Regression analysis indicated that more than 40% of the
129 In the multivariate logistic
regression analysis,
individuals living in areas with el
130 In a multivariate logistic
regression analysis,
infants treated with laser therapy
131 Cox multivariable
regression analysis investigated factors influencing fai
132 ns, and after MRI intensity normalizations a
regression analysis is used to determine a two-variable
133 Cox
regression analysis,
Kaplan-Meier curves, and cross-vali
134 In multivariable logistic
regression analysis,
MSM were more likely to present wit
135 Regression analysis (
multivariate analyses) demonstrates
136 On Cox proportional hazards
regression analysis,
normalized peak VO2 <=60%, and VE/V
137 In multivariable logistic
regression analysis,
obesity was associated with an incr
138 In multivariable
regression analysis (
odds ratio [95% CI]), summation of
139 med a systematic review and trial-level meta-
regression analysis of 3 classes of lipid-lowering thera
140 Multivariate Cox
regression analysis of a propensity score-matched sample
141 We present results from a meta-
regression analysis of data from 261 such surveys comple
142 We adapted the SPatial
REgression Analysis of DTI (SPREAD) algorithm to conduct
143 Finally,
regression analysis of our results indicated that both c
144 Cox-
regression analysis of rejection-free survival revealed
145 A random-effects meta-
regression analysis of the aggregate-level data showed a
146 We ran linear
regression analysis of the bronchial brushings transcrip
147 A multivariate linear
regression analysis of the longitudinal data for 57 anal
148 A multivariate Cox
regression analysis of the miR-21 expression in the TCGA
149 Multiple logistic
regression analysis of variables that were significant i
150 On multivariate logistic
regression analysis older patient's age, abnormal serum
151 lent direction of the plume, and conducted a
regression analysis on 2003-2016 incidence rates of thyr
152 Regression analysis on the principal components showed t
153 cess at 12 months in a multivariate logistic
regression analysis (
P = 0.006).
154 In multiple
regression analysis,
PKP (vs DALK) (odds ratio [OR]: 8.5
155 Regression analysis predicted about half of all users wi
156 In multivariable
regression analysis,
predictors of incident CKD included
157 Binary logistic
regression analysis proposed a mid-trimester biomarker p
158 = 0.99, 95% CI 0.61-1.59, I(2) = 82%), meta-
regression analysis proved that mortality was significan
159 In Cox
regression analysis,
r (voxelwise) between nSUV and rCBV
160 On Cox
regression analysis,
receiving G-CSF (hazard ratio, 0.37
161 Cox
regression analysis revealed a hazard ratio incomplete/c
162 Multiple logistic
regression analysis revealed increased likelihood of mul
163 Multivariable Cox
regression analysis revealed plasma cell dyscrasia (diff
164 Edgewise
regression analysis revealed robust negative association
165 Multivariable
regression analysis revealed significant increases in re
166 However, ordinal logistic
regression analysis revealed that a higher abundance of
167 Logistic
regression analysis revealed that age, and the dominant
168 Univariate logistic
regression analysis revealed that an adenoma count of >=
169 Multiple logistic
regression analysis revealed that combinations of Kyn co
170 Regression analysis revealed that judgments about risks
171 Multivariate logistic
regression analysis revealed that patients who were marr
172 Furthermore, multiple
regression analysis revealed that the interactions of gl
173 Multiple
regression analysis revealed TIP3 to be associated with
174 In multivariable logistic
regression analysis,
risk factors for severe infection i
175 For the MS(10,) the multivariate
regression analysis showed a significant association onl
176 Mixed-effect
regression analysis showed a wide range among the states
177 Multivariate Cox
regression analysis showed higher all-cause mortality (h
178 Regression analysis showed improvements relative to the
179 The
regression analysis showed MSM aged 21 - 25 (RR: 3.199,
180 Multivariate
regression analysis showed neutrophils, total cholestero
181 Multiple
regression analysis showed no relationship with age, gen
182 The multivariable
regression analysis showed older age (OR: 1.142, 95% CI
183 Multivariate logistic
regression analysis showed significant associations betw
184 Multivariate Cox
regression analysis showed that age and pneumonia were i
185 Multiple logistic
regression analysis showed that associated factors of my
186 Multivariate logistic
regression analysis showed that carriers with rs755622 C
187 In comparison, a subsequent whole-brain
regression analysis showed that drift, rather than diffu
188 The multiple
regression analysis showed that glucose influences the v
189 Multivariate logistic
regression analysis showed that higher the percentage of
190 Multivariate logistic
regression analysis showed that increased gestational ag
191 Further logistic
regression analysis showed that MTHFR Ala222Val genotype
192 However, multivariable
regression analysis showed that only marital status was
193 A multiple linear
regression analysis showed that only oPMN (beta = - 0.24
194 Logistic
regression analysis showed that OSFI results of 0.3 or m
195 The multivariate
regression analysis showed that periodontitis was the on
196 Multivariable proportional odds
regression analysis showed that race was a statistically
197 A logistic
regression analysis showed that the following variables
198 Multilevel
regression analysis showed that the probability of impla
199 Multivariable logistic
regression analysis showed that washout of greater than
200 Multivariate logistic
regression analysis showed women with hydrosalpinx were
201 The present meta-
regression analysis sought to estimate the dose-response
202 Partial least squares
regression analysis suggested that BG and MnP activities
203 In multivariable Cox
regression analysis,
SVR was associated with a reduction
204 rfectly consistent with a more sophisticated
regression analysis technique based on Taylor and Fourie
205 ted via bootstrap simulation, i.e., a robust
regression analysis that is the appropriate statistical
206 In a logistic
regression analysis,
the integrity of the anterior DMN a
207 On binary logistic
regression analysis,
the Kono-S anastomosis was the only
208 In multivariable logistic
regression analysis,
the odds of pCR for patients who ha
209 Cox
regression analysis (
time-dependent) was used to evaluat
210 We further used multivariate Cox
regression analysis to assess miR-21 expression in the T
211 variate model was constructed using logistic
regression analysis to assess the ability of MRI to pred
212 We performed logistic
regression analysis to assess the risk of in-hospital AK
213 In mice, we applied
regression analysis to compare left and right gait metri
214 r local sedation using multivariate logistic
regression analysis to control for potentially confoundi
215 signs of IH were entered in binary logistic
regression analysis to determine a predictive score of s
216 We used Cox
regression analysis to determine clinically significant
217 We used multivariable Cox-
regression analysis to determine whether surgical approa
218 We used multiple
regression analysis to estimate predictors of pain relie
219 d disease duration and (iii) weighted linear
regression analysis to estimate the effect of age, sex a
220 We performed multiple logistic
regression analysis to estimate the odds (ORs) of prior
221 transcriptomic datasets through elastic net
regression analysis to identify a gene signature that ca
222 archical cluster analysis, and also used Cox
regression analysis to identify associations with early
223 We performed logistic
regression analysis to identify factors associated with
224 Multivariable
regression analysis to identify predictors of LVEF impro
225 Moreover, we perform
regression analysis to investigate how these cultural st
226 r Genome Atlas (TCGA) using the on-line gene
regression analysis tool GRACE.
227 Logistic
regression analysis,
used to determine factors associate
228 All studies performed linear
regression analysis using an additive genetic model and
229 Logistic
regression analysis was applied to estimate the risk of
230 Regression analysis was applied to investigate the assoc
231 Multivariable linear
regression analysis was applied to study the difference
232 Multivariable
regression analysis was conducted to identify demographi
233 ecological (at department level) multilevel
regression analysis was conducted to identify the major
234 Logistic
regression analysis was consistent with the primary anal
235 Logistic
regression analysis was done independently by geographic
236 Multivariable logistic
regression analysis was performed adjusting for age, sex
237 A linear
regression analysis was performed and linear multi-depen
238 Conditional logistic
regression analysis was performed and odds ratios (ORs)
239 C) analysis, odds ratios and binary logistic
regression analysis was performed double-blind.
240 Multivariable logistic
regression analysis was performed in a stepwise fashion
241 Logistic
regression analysis was performed on the barriers that c
242 Binary logistic
regression analysis was performed to assess association
243 A multivariable logistic
regression analysis was performed to assess factors rela
244 Multivariable Cox
regression analysis was performed to determine predictor
245 Multiple logistic
regression analysis was performed to determine the indep
246 Multiple
regression analysis was performed to determine whether a
247 A Cox
regression analysis was performed to evaluate the risk f
248 Logistic
regression analysis was performed to examine the factors
249 Poisson multivariable
regression analysis was performed to identify predictors
250 A multivariable logistic
regression analysis was performed to identify predictors
251 Multivariable
regression analysis was performed to identify risk facto
252 Interrupted time series
regression analysis was performed to identify the annual
253 A binary logistic
regression analysis was performed to identify the risk f
254 Cox
regression analysis was performed to investigate the ass
255 Regression analysis was performed to test the independen
256 aining sample (where a multivariate logistic
regression analysis was run) and a validation sample (wh
257 Weighted multivariable logistic
regression analysis was then used to develop a nomogram
258 Multivariate logistic
regression analysis was undertaken to identify associati
259 Logistic
regression analysis was undertaken to identify independe
260 Meta-
regression analysis was used to assess the association o
261 Linear multiple
regression analysis was used to create models for estima
262 Linear
regression analysis was used to determine factors associ
263 management, and inverse probability weighted
regression analysis was used to determine factors associ
264 Multivariate Cox
regression analysis was used to determine the predictive
265 Further, partial least squares
regression analysis was used to estimate chemical concen
266 Multivariate Cox
regression analysis was used to estimate risk-adjusted p
267 Poisson
regression analysis was used to estimate the incidence r
268 Generalized estimating equations logistic
regression analysis was used to evaluate both MR fingerp
269 A Cox proportional hazards
regression analysis was used to evaluate factors indepen
270 Competing risk
regression analysis was used to evaluate the capability
271 Logistic
regression analysis was used to evaluate the diagnostic
272 Multivariate logistic
regression analysis was used to evaluate the odds ratio
273 Multiple
regression analysis was used to evaluate the relationshi
274 Multivariate ordinal logistic
regression analysis was used to predict the presence of
275 Linear
regression analysis was used to study associations betwe
276 Zero-inflated negative binomial
regression analysis was used to test for an association
277 Backward multivariate logistic
regression analysis was utilized to specify the variable
278 Using multivariable linear
regression analysis,
we determined that multiple noncova
279 Using weighted linear
regression analysis,
we found a strong relationship betw
280 In a multivariable logistic
regression analysis,
we investigated the risk of IE acco
281 ized-linear models (GLM) and multi-level Cox-
regression analysis were applied.
282 Two-sided t-tests and
regression analysis were performed to compare these data
283 Sub analyses using logistic
regression analysis were performed to evaluate the impac
284 Univariate and multivariate logistic
regression analysis were performed to identify periopera
285 stic regression and Cox proportional hazards
regression analysis were performed to investigate risk f
286 Chi-square testing and
regression analysis were performed using IBM SPSS softwa
287 Univariate and multivariate Cox
regression analysis were performed, and predictive model
288 t hoc Bayesian analysis and a mixed logistic
regression analysis were performed.
289 earson correlation, and multivariable binary
regression analysis were used for statistical analyses.
290 The Kaplan-Meier estimator, U test, and Cox
regression analysis were used for statistics.
291 The coefficients from the
regression analysis were used to construct a calculator
292 l a more favorable approach than traditional
regression analysis when estimating causal effects using
293 f less than 0.1 were considered for logistic
regression analysis which identified predictors of morta
294 patient outcomes was assessed using logistic
regression analysis with backward selection strategy.
295 A linear
regression analysis with generalized estimating equation
296 Using multivariate logistic
regression analysis with leave-1-out cross validation, w
297 Based on multivariate
regression analysis with qRT-PCR as the gold standard, f
298 ix clinical outcomes were analyzed using Cox
regression analysis with rivaroxaban as the reference.
299 Multivariable logistic
regression analysis,
with synthetic minority oversamplin
300 Passing-Bablok
regression analysis yielded a good linear correlation, w