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1 on risk was evaluated by multilevel logistic regression analysis.
2 ndependent t test, Wald chi(2), and binomial regression analysis.
3 ed using survey weighted logistic and linear regression analysis.
4 he following 24 hours, we performed logistic regression analysis.
5 nosis of cognitive impairment using logistic regression analysis.
6 ate ratios (IRRs) were calculated by Poisson regression analysis.
7 was assessed by sex, within each site, using regression analysis.
8    Case-control differences were tested with regression analysis.
9 ared with healthy controls using mixed-model regression analysis.
10 ng characteristics was assessed by sex using regression analysis.
11 uated using a random-effect model and a meta-regression analysis.
12  significant predictors in the multivariable regression analysis.
13 dent cancer were examined using adjusted Cox regression analysis.
14  using multivariable Cox proportional hazard regression analysis.
15 I, 1.11-3.08) persisted on multivariable Cox regression analysis.
16 her random forest, decision tree or logistic regression analysis.
17 nd overall change in BCVA was assessed using regression analysis.
18 al scaling parameters obtained from a linear regression analysis.
19 associated with 30-day mortality in logistic regression analysis.
20  the risk of 6-month sc-AR in a multivariate regression analysis.
21 al were analyzed using multivariate logistic regression analysis.
22 ups were performed using multivariate linear regression analysis.
23  using bivariable and multivariable logistic regression analysis.
24 elated with vascular occlusion with logistic regression analysis.
25  the cohorts by using mixed-effects logistic regression analysis.
26 Spearman correlation coefficients and linear regression analysis.
27 % CI, 1.65-33.66; P = .009) in multivariable regression analysis.
28 -rank survival analysis and multivariate Cox regression analysis.
29        Additional factors were analysed by a regression analysis.
30 res in right eyes were assessed using linear regression analysis.
31 elopment were evaluated via multivariate Cox regression analysis.
32 iable linear model for GFR using statistical regression analysis.
33 was developed using a stepwise multivariable regression analysis.
34 d Gross Domestic Product [GDP]) using linear regression analysis.
35  phantom experiment by using multiple linear regression analysis.
36  This study was a systematic review and meta-regression analysis.
37  of the trends was evaluated from join-point regression analysis.
38  of therapy were identified using a logistic regression analysis.
39 nfidence interval [CI], .38-1.48) in the Cox regression analysis.
40 ic surgery or CR-POPF occurrence on logistic regression analysis.
41  interval, 0.84-0.99) in a multivariable Cox regression analysis.
42 acer binding in the PD group on multivariate regression analysis.
43 rn to work were assessed using Fine and Gray regression analysis.
44  dependent variables, using backwards linear regression analysis.
45                         On multivariable Cox regression analysis, 3 preoperatively available factors
46    In IPTW-adjusted Cox proportional hazards regression analysis, AC was associated with a significan
47 s of AT were identified by multivariable Cox regression analysis accounting for left truncation.
48 ng ISBCS to DSBCS using conditional logistic regression analysis, accounting for surgeon and patient-
49                                              Regression analysis adjusted for clustered observations
50 rest in the VLSM model, including a multiple regression analysis adjusted for confounding variables.
51                    Results were confirmed in regression analysis adjusted for team composition.
52                          Principal component regression analysis, adjusted for confounders, showed th
53 n-atopic children were estimated by logistic regression analysis adjusting for potential confounders.
54                                     A linear regression analysis adjusting for sex, age and body mass
55 6; 95% CI, 2.43-66.88; P = .003) on logistic regression analysis after controlling for preoperative v
56 re evaluated using a Cox proportional hazard regression analysis after propensity score matching.
57                                           In regression analysis, age (OR, 1.06 [95% confidence inter
58                      In single-predictor Cox regression analysis, age, disease stage, tumor weight, s
59                                   Based on a regression analysis, age, sex, and previous diagnosis of
60                                          The regression analysis also showed that there was a good li
61 thickness (cCIMT) using multivariable linear regression analysis among 1554 African Americans from ME
62                                 Multivariate regression analysis and receiver operating characteristi
63 d data analyzed with one-way ANOVA, logistic regression analysis and receiver-operating characteristi
64                         We also did post-hoc regression analysis and subgroup analysis of children by
65 OP and medications at one year with a linear regression analysis and survival with log-rank testing.
66                                  We used Cox regression analysis and the landmark approach to investi
67 ortality and cardiovascular mortality by Cox regression analysis and with severity of disease by gene
68 kers was calculated through bivariate linear regression analysis, and the association between ocular
69                     Analysis was done by Cox regression analysis, ANOVA, and chi(2).
70                                Multivariable regression analysis assessed whether low protein intakes
71                                      Second, regression analysis assumes that a single effect estimat
72                                        Egger regression analysis between the traits suggests that per
73                        Correlation analysis, regression analysis, Bland-Altman plot, and paired sampl
74 orrelated with VA in univariate and multiple regression analysis (both P < 0.001).
75 d gait difficulty motor PD subtype in linear regression analysis, but staging of alpha-synuclein path
76 ture selection and prediction performance in regression analysis, but there has been limited work on
77  increased mean diffusivity were tested with regression analysis by controlling for age.
78                                  On multiple regression analysis, choroidal thickness, age, and disea
79                                Multivariable regression analysis confirmed that high-burden hospitals
80 tors were identified using multivariable Cox regression analysis: connective tissue disease (hazard r
81                                    After Cox regression analysis controlling for age, tumor size, res
82        Univariate and mixed-effects logistic regression analysis controlling for center effect were u
83                    In mixed-effects logistic regression analysis controlling for patient, injury, cli
84                                              Regression analysis, correlation coefficient analysis, a
85                                       Linear regression analysis demonstrated a continuous relationsh
86                                              Regression analysis demonstrated that each type of traum
87                                              Regression analysis demonstrated that each type of traum
88                                          Cox regression analysis demonstrated that factors independen
89                              Spline function regression analysis demonstrated that if dwell time exce
90                                         Meta-regression analysis demonstrated that probiotics were si
91                                       Linear regression analysis demonstrated that skin sodium conten
92                                       Linear regression analysis demonstrated that the drug depot cha
93                        Multivariate logistic regression analysis demonstrated that younger age (60-79
94                          The use of multiple regression analysis demonstrates that FAEE content can b
95                                     Logistic regression analysis determined the predictors of AKI.
96                                       In Cox regression analysis, elderly recipients of elderly DCD k
97                         In multiple logistic regression analysis, elderly recipients of elderly DCD k
98                                          Cox regression analysis estimated the instantaneous hazard o
99                                           On regression analysis, every increase in CLS of 1.9 correl
100                                          Cox regression analysis explored risk factors for interim de
101                          Results At multiple regression analysis, fibrosis was the only variable asso
102 emission and zero-inflated negative binomial regression analysis for alcohol consumption.
103                Pearson's correlation, linear regression analysis for clinical outcome parameter and l
104  for clinical outcome parameter and logistic regression analysis for postsurgical complication rates
105                             We used logistic regression analysis for remission and zero-inflated nega
106 confirmed as an independent predictor in Cox regression analysis (hazard ratio, 1.97 [95% CI, 1.18-3.
107                       After multivariate Cox-regression analysis, higher PDRI (hazard ratio [HR], 1.6
108                             Further multiple regression analysis identified certain pre-extracorporea
109                            Multivariable Cox regression analysis identified that Model A or Model B h
110                                     Multiple regression analysis identified that number of SPT visits
111                  Moreover, multivariable Cox regression analysis identified the combination of B3GALT
112         We employed Cox proportional hazards regression analysis in age- and multivariable-adjusted m
113 associated with outcome by multivariable Cox regression analysis, in addition to age, NT-proBNP serum
114 isk factors at baseline (univariate logistic regression analysis) included longer total duration of u
115                                       Binary regression analysis including these variables found no s
116                 In the multivariate logistic regression analysis, increased time to surgery was not a
117                            Negative binomial regression analysis indicated that the participants in t
118                                              Regression analysis indicated that the pesticide concent
119                          In multivariate Cox regression analysis, interaction between use of sunitini
120                             On multivariable regression analysis, LA conduit strain emerged as strong
121                                   A multiple-regression analysis led to a final model explaining FLI
122  chi(2) test; 4) Kruskal-Wallis test; and 5) regression analysis; level of significance alpha = 5%.
123                     On multivariate logistic regression analysis, LPF-VT was more often associated wi
124                                       In Cox regression analysis, neither increasing the number of ur
125                     In multivariate logistic regression analysis, neither LPI location nor LPI area n
126                             On multivariable regression analysis, obesity and ascites were associated
127 to 0.14; P = .49) nor by univariate logistic regression analysis (odds ratio, 0.64; 95% CI, 0.22-1.67
128 0.22-1.67; P = .37) or multivariate logistic regression analysis (odds ratio, 1.09; 95% CI, 0.34-3.28
129 hospital mortality in multivariable logistic regression analysis (odds ratio, 4.4 [95% CI, 1.4-13.5];
130                                       Linear regression analysis of all cases indicated that spine de
131                            We did a logistic regression analysis of cannabis use from retrospective d
132 of the LS genes by using polytomous logistic regression analysis of clinical and germline data from 1
133 t SCNA, we describe a method termed "Genomic Regression Analysis of Coordinated Expression" (GRACE) t
134 ious hypotheses we performed a logistic meta-regression analysis of cure rates from all falciparum ma
135                                     Multiple regression analysis of dose versus root growth inhibitio
136                             Multivariate Cox regression analysis of MIPI before postibrutinib treatme
137                                              Regression analysis of predictive model simulation resul
138                                              Regression analysis of search pattern, search technique,
139 s (SIRs) and, for SCC, multivariable Poisson regression analysis of SIR ratios, adjusting for 5-year
140                                 Using linear regression analysis of spectral integral values, 4 count
141 een for association with VTE; competing risk regression analysis of time to recurrent VTE was conduct
142                            In competing risk regression analysis of time to recurrent VTE, TF remaine
143  compared with those obtained from nonlinear regression analysis of time-activity curves.
144                                     Censored regression analysis on all affected and unaffected at-ri
145                           After multivariate regression analysis, only early intravenous catheter rem
146                          In multivariate Cox regression analysis, only LV ejection fraction (EF) and
147                  In a multivariable logistic regression analysis, only moderate to severe bronchial h
148 alyzed by Kaplan-Meier log-rank test and Cox regression analysis ( P < 0.05).
149  analysis (P </= 0.018) but not the multiple regression analysis (P >/= 0.210).
150 icantly correlated with VA in the univariate regression analysis (P </= 0.018) but not the multiple r
151 l long-term outcome in multivariate logistic regression analysis (P = 0.04; odds ratio, 0.25; 95% con
152 anded Disability Status Scale score in a Cox regression analysis (per 1-SD increase in MSIS-29-PHYS s
153                             On multivariable regression analysis, permanent injuries were more often
154  a web interface that automates the LD score regression analysis pipeline.
155                     On multivariate logistic regression analysis, PSMA and serum PSA significantly co
156                       Correlation and linear regression analysis reveal a strong association between
157                                              Regression analysis revealed a lower risk of decompensat
158                                     However, regression analysis revealed a stronger association betw
159   Primary multivariable conditional logistic regression analysis revealed an association between epit
160                            Multivariable Cox regression analysis revealed GLS and LAVI to be independ
161                       However, multivariable regression analysis revealed no significant differences
162                            Multiple logistic regression analysis revealed older age and higher vertic
163                                          Cox regression analysis revealed that BK was a significant f
164                                          Cox regression analysis revealed that elevated PDW was an in
165                      Multivariate logistical regression analysis revealed that failure of H. pylori e
166                                        A Cox-regression analysis revealed that mortality was much hig
167                                     Logistic regression analysis revealed that only male gender (odds
168                                          Cox regression analysis revealed that reduced MPV was an ind
169                                          Cox regression analysis revealed that the amount of residual
170                                     Multiple regression analysis revealed that, controlling for age,
171                                              Regression analysis revealed the two strongest independe
172                         In multivariable Cox regression analysis, Share 35 was associated with improv
173                                Multivariable regression analysis showed an adjusted hazard ratio of 2
174    Introducing these variables to a logistic regression analysis showed areas under the receiver-oper
175                                         Meta-regression analysis showed lower risk of death in observ
176                                 Multivariate regression analysis showed no correlation between high-r
177                                     Logistic regression analysis showed that a decrease in ONH diamet
178                                              Regression analysis showed that a positive SNAQ/MUST sco
179                              Multiple linear regression analysis showed that apparent amylose content
180                                              Regression analysis showed that cerebellar metrics accou
181                            Multiple logistic regression analysis showed that female gender, older age
182               Results from stepwise logistic regression analysis showed that five biomarkers (20-Hydr
183                    Multiple ordinal logistic regression analysis showed that higher white matter hype
184                            Multivariable Cox regression analysis showed that intrahospital CVEs (HR,
185                            Multivariable Cox regression analysis showed that intrahospital Pneumonia
186                                          Cox regression analysis showed that macrovascular invasion (
187                                          Cox regression analysis showed that MPV was an independent p
188                       Multivariable logistic regression analysis showed that neither contrast osmolal
189                                     Our meta-regression analysis showed that none of the factors cons
190                                              Regression analysis showed that reduction in connectivit
191                                   The linear regression analysis showed that stroke-related erectile
192          Univariate Cox proportional-hazards regression analysis showed that the CTC count in PPB or
193                                              Regression analysis showed that the severity of ocular p
194                            Multivariable Cox regression analysis showed that the third versus the fir
195                                     Multiple regression analysis showed that the timing of food intak
196                                              Regression analysis showed the effect on executive atten
197                                 Furthermore, regression analysis shows a positive association between
198  scattered over the study area, but logistic regression analysis suggested a propensity of these infe
199                        Multivariate logistic regression analysis suggested that A*02:01 and DRB1*11:0
200                            Notably, logistic regression analysis suggested that DQ8/8 patients had an
201                            Multivariable Cox regression analysis tested the relationship between avai
202 region of interest-based multivariate linear regression analysis that was adjusted for potential conf
203                                  In multiple regression analysis, the association of a treatment regi
204              Prior to multivariable logistic regression analysis, the association of each independent
205 ording to the hazard ratios in multivariable regression analysis, the CMR risk score was created by a
206                     On multivariate logistic regression analysis, the factors predicting survival wer
207                                On univariate regression analysis, the hazard ratios (HRs) associated
208                          In multiple Poisson regression analysis, the incidence rate ratio in the emp
209                         On multiple variable regression analysis, the number of nonviable segments wa
210     On multivariate Cox proportional hazards regression analysis, the presence of vitreous haze had a
211  of iron status and assesses the impact of a regression analysis to adjust for inflammation on estima
212 f resection margin status on survival, and a regression analysis to analyze positive resection margin
213 ssover study design and conditional logistic regression analysis to assess associations between the r
214                                  We used Cox regression analysis to assess differences in risk for HF
215            We performed multinomial logistic regression analysis to assess the weighting of histologi
216 to non-users, we used multivariable logistic regression analysis to calculate odds ratios (OR) with 9
217                                  We used Cox regression analysis to calculate the hazard ratio (HR) o
218 ed a difference-in-differences multivariable regression analysis to compare changes in prescribing by
219 mber 31, 2015, used propensity score-matched regression analysis to compare quarterly changes in the
220                      We used multiple linear regression analysis to compare SMC with GES, adjusting f
221 ty, and geographic region; multiple logistic regression analysis to determine independent association
222 correlations between biomarkers and logistic regression analysis to determine the predictive value of
223                                      Boosted regression analysis to determine the relative influence
224                 We also performed a logistic regression analysis to determine the variables associate
225                  We performed a multivariate regression analysis to estimate the burden of RSV in chi
226 llus PCR results, subjected to multilogistic regression analysis to identify a best-fit model for pre
227 f less than 0.1 were considered for logistic regression analysis to identify predictors of mortality.
228          We conducted multivariable logistic regression analysis to identify risk factors.
229                              We did logistic regression analysis to obtain odds ratios (ORs) for the
230 in cancer were tested using multivariate Cox regression analysis to yield adjusted hazard ratios (HR)
231                         In multivariable Cox regression analysis, treatment with either regimen (haza
232                                In a logistic regression analysis, ulcers were identified to be a sign
233  than CC-genotype patients, according to Cox regression analysis (univariate P = .040 and multivariab
234                       Multivariable logistic regression analysis, using the calculated propensity sco
235                                      In meta-regression analysis, variables significantly associated
236 disease, and a pooled multivariable logistic regression analysis was conducted for each group.Iron de
237                            Multiple logistic regression analysis was conducted to determine if increa
238                                Fine and Gray regression analysis was conducted to determine the adjus
239 ow concentration hCG protein assay in linear regression analysis was GO-peptide (1mM): GO-peptide (0.
240   A retrospective single-center multivariate regression analysis was performed for adult patients und
241                        Multivariate logistic regression analysis was performed for association of cli
242 DP, LBMMR-AC, and LBMFormula Further, linear regression analysis was performed on LBMMR-AC and LBMADP
243                                     Logistic regression analysis was performed to assess potential pr
244                                          Cox regression analysis was performed to assess the adjusted
245                                     Multiple regression analysis was performed to assess the associat
246               A hierarchical multiple linear regression analysis was performed to assess the relation
247                               Competing risk regression analysis was performed to calculate the risks
248                        Age-adjusted logistic regression analysis was performed to compare the grade-s
249                    Multivariable Cox hazards regression analysis was performed to determine factors a
250                       Multivariable logistic regression analysis was performed to determine variables
251                     Cox proportional hazards regression analysis was performed to estimate HRs by usi
252                       A conditional logistic regression analysis was performed to evaluate the associ
253                                     Logistic regression analysis was performed to evaluate the sensit
254                              Binary logistic regression analysis was performed to identify factors as
255                                       Linear regression analysis was performed to identify trends in
256         A hospital-level multivariate linear regression analysis was performed while controlling for
257                                   A logistic regression analysis was performed with uveitis as the ma
258      Multivariable linear, logistic, and Cox regression analysis was performed.
259 nd overall survival, and a multivariable Cox regression analysis was performed.
260                                Mixed-effects regression analysis was then performed to determine whet
261                                              Regression analysis was used in the development of table
262                             Multivariate Cox regression analysis was used to account for the influenc
263                       Multivariable logistic regression analysis was used to adjust for perinatal and
264                                          Cox regression analysis was used to assess the effects of vo
265                                     Multiple regression analysis was used to compare changes over tim
266                                          Cox regression analysis was used to compute 1- to 35-year ad
267                            Multiple logistic regression analysis was used to determine independent pa
268                                     Logistic regression analysis was used to determine significant de
269                                     Multiple regression analysis was used to determine the adjusted a
270                     Cox proportional hazards regression analysis was used to determine the associatio
271                                          Cox regression analysis was used to determine the associatio
272                                              Regression analysis was used to determine the factors as
273 nivariate followed by multivariable logistic regression analysis was used to develop a parsimonious c
274                            Multiple logistic regression analysis was used to estimate adjusted odds r
275                                 Log-binomial regression analysis was used to estimate relative risks
276 ersons aged 60 y and over; negative binomial regression analysis was used to estimate the time trend.
277                                     Logistic regression analysis was used to evaluate pretest variabl
278                                Multivariable regression analysis was used to evaluate the impact of i
279                               General linear regression analysis was used to examine the association,
280                                              Regression analysis was used to explore the characterist
281                            Multiple logistic regression analysis was used to explore the risk factors
282                             Multivariate Cox regression analysis was used to identify covariates asso
283                                    Piecewise regression analysis was used to identify relative change
284                             Cox multivariate regression analysis was used to identify risk factors fo
285                                     Logistic regression analysis was used to identify the independent
286                                Multivariable regression analysis was used to measure the associations
287                                       Linear regression analysis was used to test the association of
288                In this multivariable ordinal regression analysis, we collected data from a cross-sect
289                                         With regression analysis, we estimated that to reduce HIV inc
290             In a systematic review with meta-regression analysis, we found evidence that administrati
291                        Based on multivariate regression analysis, we found that certain intrinsic kin
292                                 Using linear regression analysis, we were able to quantify the deposi
293 effects meta-analyses with subgroup and meta-regression analysis were performed.
294       Chi-squared tests and multivariate Cox regression analysis were performed.
295          Descriptive statistics and logistic regression analysis were performed.
296               Multivariable ordinal logistic regression analysis with an interaction term was used to
297                             At pairwise meta-regression analysis with either study origin from Asia o
298 stic models were developed by using logistic regression analysis with gestational age as a covariate.
299 ce intervals determined by means of logistic regression analysis with pairwise comparisons.
300                                       On Cox regression analysis, younger age was independently assoc

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