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1 through polynomial regressions] and advanced regression analyses.
2 tions were studied using univariate logistic regression analyses.
3 tion, area under the curve (AUC), and linear regression analyses.
4  significant difference in adjusted logistic regression analyses.
5  Residual confounding was controlled through regression analyses.
6 s were tested using Mann-Whitney U tests and regression analyses.
7 eneity using subgroup, sensitivity, and meta-regression analyses.
8 tion with overall survival (OS) based on Cox-regression analyses.
9  selection bias and limited the power of the regression analyses.
10              These results were confirmed by regression analyses.
11  buffer zones, through multivariable Poisson regression analyses.
12 reened using backward multivariable logistic regression analyses.
13 and FLD were assessed in linear and logistic regression analyses.
14  post-CABG PCI were assessed in multivariate regression analyses.
15 ing time-updated, multivariable-adjusted Cox regression analyses.
16 were identified using multivariable logistic regression analyses.
17 ncer were assessed in multivariable logistic regression analyses.
18 xamined by single-marker and multimarker Cox regression analyses.
19 ere determined using Cox proportional hazard regression analyses.
20 loss (DCGL) were examined using adjusted Cox regression analyses.
21 descriptive, frequencies, correlational, and regression analyses.
22  complete data and were included in adjusted regression analyses.
23 atios (OR) for CAD from conditional logistic regression analyses.
24 ed itself as varying slopes in the per-pixel regression analyses.
25 ibution of PRS(313) was quantified using Cox regression analyses.
26 model was used, along with subgroup and meta-regression analyses.
27  signal was compared with NWU using logistic regression analyses.
28 gical outcomes in univariate or multivariate regression analyses.
29  ventricle atrophy using nested multivariate regression analyses.
30 nate using spatial and multivariate logistic regression analyses.
31 le types by Bland-Altman and weighted Deming regression analyses.
32 es (on mesial and distal) via mixed-modeling regression analyses.
33  Univariate tests and multivariable logistic regression analyses.
34 ure was conducted for multivariable logistic regression analyses.
35 reened using backward multivariable logistic regression analyses.
36 ctors were investigated by multivariable Cox regression analyses.
37 neurofilament light chain (NfL) using linear regression analyses.
38         Measurements were assessed by linear regression analyses: a between-group comparison of ferum
39  starting treatment were investigated by Cox regression analyses according to an a priori analysis pl
40                                     Logistic regression analyses adjusted for age, sex, ethnicity, sm
41             Second, using the entire sample, regression analyses adjusted for baseline and medical co
42            We conducted conditional logistic regression analyses adjusted for body mass index, smokin
43                                          Cox regression analyses adjusted for Charlson comorbidity in
44                                          Cox regression analyses adjusted for Charlson comorbidity in
45        For each metabolite measure, logistic regression analyses adjusted for gender, age, smoking, f
46 ions remained in multivariable mixed-effects regression analyses adjusted for known clinicopathologic
47                                       In Cox regression analyses adjusted for risk factors, higher le
48                                              Regression analyses (adjusted for year of birth, sex, th
49                            Multivariable Cox regression analyses, adjusted for potential confounders,
50                                     Logistic regression analyses adjusting for age, all the sociodemo
51   Smooth curve fitting and multiple logistic regression analyses adjusting for age, sex, betel nut co
52 o treat by means of multilevel random effect regression analyses adjusting for clustering in health c
53                 Univariate and multivariable regression analyses adjusting for demographics, cardiova
54 d between groups using multivariate logistic regression analyses, adjusting for maternal age, ethnici
55                             In mixed-effects regression analyses, admission to a hospital with end-of
56                      On multivariable linear regression analyses after adjusting for different variab
57 ]PBR28 signal and NA was assessed first with regression analyses against Beck Depression Inventory (B
58 ive, sequence pattern analyses, and logistic regression analyses) aimed to detect any combinations of
59                                Multivariable regression analyses and 2-sample regression-based Mendel
60 adiomics features were selected by Lasso-Cox regression analyses and a separate radiomics signature w
61 tooth survival were assessed via multi-level regression analyses and Cox Proportional-Hazards Models.
62 tion were examined with multivariable linear regression analyses and cross-lagged modeling.
63                            Adjusted logistic regression analyses and generalized estimating equations
64                                          Cox regression analyses and incidence density rates for card
65                      Both case-only logistic regression analyses and polytomous logistic regression a
66                LnCeVar-Survival performs COX regression analyses and produces survival curves for var
67 also selected in the discriminant and linear regression analyses, and could be used as potential biom
68              We used unadjusted and adjusted regression analyses as well as propensity score matching
69       Univariable and multivariable logistic regression analyses assessed the association of clinical
70                                              Regression analyses assessed the effects of antibody ser
71                                     Logistic regression analyses based on a conceptual model of DR ri
72                    In multivariable logistic regression analyses, baseline severe disease by Infectio
73                                       Linear regression analyses before and after multivariable adjus
74  multivariable-adjusted conditional logistic regression analyses, better adherence to the Mediterrane
75                                  In multiple regression analyses, bulky lymphadenopathy (>=5 cm) and
76 intracountry risk-adjusted UR trend logistic regression analyses, can be translated to other internat
77                                       In Cox regression analyses, CEC was significantly associated wi
78                       In propensity-adjusted regression analyses, clinical new-onset atrial fibrillat
79                                       Linear regression analyses compared LC characteristics between
80                                   The Deming regression analyses comparing the accuracy of the BD FAC
81 ertility and overall infertility through Cox regression analyses comparing the firefighters with 2 re
82                                              Regression analyses controlling for age were performed t
83           We used descriptive statistics and regression analyses (controlling for the number of autho
84                                              Regression analyses demonstrated a trend for more leakag
85                                       Linear regression analyses demonstrated lower FGF23 levels in n
86                         Multivariable linear regression analyses demonstrated that non-White patients
87                               Univariate Cox regression analyses detected age, waist-to-hip ratio (WH
88                           However, moderated regression analyses emphasize that increased symptom sev
89                            In univariate Cox regression analyses, estimated glomerular filtration rat
90               Finally, we conducted logistic regression analyses estimating associations of demograph
91                                              Regression analyses examined how political preferences i
92                                     Logistic regression analyses examined the association between pre
93                                 Multivariate regression analyses examined the association of usual ma
94                                     Logistic regression analyses examined the number of past-year sui
95 r-regions, with adjusted linear and logistic regression analyses examining associations with immune p
96              Results of multivariable linear regression analyses examining the Systemic Lupus Activit
97  their stroke, were included in two logistic regression analyses examining which features were indepe
98                      In multivariable linear regression analyses, ferritin (B = -0.43), transferrin s
99    We performed sparse partial least-squares regression analyses followed by ordinary least-squares r
100                                          Cox regression analyses for patient and graft survival (cens
101                         Kaplan-Meier and Cox regression analyses for the overall risk of nAMD develop
102                                     Logistic regression analyses found a significant effect of PePS o
103                  Using multivariate logistic regression analyses four SNPs were significantly associa
104                                 Hierarchical regression analyses further show that variations in spat
105         Univariate and multivariate logistic regression analyses have been performed for both implant
106                                  Elastic net regression analyses identified 26 activity, functional c
107                            Adjusted logistic regression analyses identified metabolites and modules o
108 al partial least squares and multiple linear regression analyses identified the spectral slope coeffi
109              In inverse probability weighted regression analyses, implementing the best practice advi
110 n at 5 y were analyzed using multiple linear regression analyses in 4 adjustment models for each outc
111 eline) at week 8 was developed with logistic regression analyses in the CO-MED trial using participan
112 ivariable Kaplan-Meier and multivariable Cox regression analyses in the unmatched consecutive cohort
113     We performed univariate analyses and Cox regression analyses including important predictors on un
114               We conducted negative binomial regression analyses, including city as a random effect.
115                        Multivariate logistic regression analyses indicated BMI of 11.7-23.3 kg/m(2) (
116 from age- and race/ethnicity-adjusted linear regression analyses indicated modest, but statistically
117                                              Regression analyses indicated significantly higher (p <
118            Results from multivariable linear regression analyses indicated that serum concentrations
119                                     Multiple regression analyses indicated that the extent to which a
120                                     Multiple regression analyses indicated that the HIV x push-ups in
121 sion repeatability were assessed with linear regression analyses, intraclass correlation coefficients
122 endall tau correlation, multivariable linear regression analyses, Kruskal-Wallis rank sum test, and p
123                         In multivariable Cox regression analyses, lower levels of TSAT (hazard ratio
124                            In univariate Cox regression analyses, male sex, older age, and recipients
125                                           In regression analyses, models comprising significant varia
126                                  We did meta-regression analyses of annual visits and admissions per
127  performing dynamic correlation and multiple regression analyses of IQGAP1 scaffold mutants.
128                                 Post hoc Cox regression analyses of outcomes by baseline HF history w
129                                Multivariable regression analyses of the mean SR versus the mean blood
130 ariants as determinants, we performed linear regression analyses on the residuals of the postprandial
131 otential prognostic variables using logistic regression analyses, partially adjusted for age, sex, sm
132                                 Using linear regression analyses, patients having received at least t
133 least squares regression and multiple linear regression analyses prioritized three water quality para
134                                     Logistic regression analyses provided information on the influenc
135 the HR, were assessed using Logistic and Cox regression analyses, respectively.
136 re investigated through Kaplan-Meier and Cox regression analyses, respectively.
137 llow-up, by limiting FDR-corrected p < 0.05, regression analyses revealed 180/228 apoE-polymorphism-r
138                      The simple and multiple regression analyses revealed a significant but weak pred
139                    Results of multilevel Cox regression analyses revealed a statistically significant
140                                     Logistic regression analyses revealed a strong/independent associ
141                                          Cox regression analyses revealed an elevated risk of lung an
142                              Adjusted linear regression analyses revealed associations between period
143                                 Multivariate regression analyses revealed perceived drunkenness and v
144                                              Regression analyses revealed specific significant relati
145                                       Linear regression analyses revealed that baseline buccal bone t
146                                              Regression analyses revealed that fractional anisotropy
147                        Multivariate logistic regression analyses revealed that major complications we
148                            Subgroup and meta-regression analyses revealed that medication use, medica
149                                              Regression analyses revealed that the severity of impair
150                                              Regression analyses revealed that this combination of fa
151                                 Hierarchical regression analyses revealed that type of work, changes
152                               Passing-Bablok regression analyses revealed the sxtA assay to overestim
153                                 Multivariate regression analyses show that large-scale indices of ENS
154                                              Regression analyses showed a negative correlation betwee
155                                              Regression analyses showed little difference in odds rat
156                        Multivariate logistic regression analyses showed older age (odds ratio [OR] pe
157                                       Deming regression analyses showed point estimates for slopes ge
158                            Furthermore, meta-regression analyses showed that age, gender and sample s
159 onths after ICU admission, multivariable Cox regression analyses showed that case-mix adjusted hazard
160                              Multiparametric regression analyses showed that in COVID-19-infected pat
161                                     Bayesian regression analyses showed that on average, active learn
162                        Multivariate logistic regression analyses showed that people who are currently
163                                     Logistic regression analyses showed that the clip use did not mod
164                        Zero-inflated Poisson regression analyses showed that the likelihood of taking
165                                              Regression analyses showed that, on both tasks, the more
166 a significant moderator in subgroup and meta-regression analyses (slope beta = -0.16; 95% CI, -0.29 t
167 rt: 1.16 [0.74-1.82]; p=0.52); multivariable regression analyses stratified by age group yielded simi
168                          Bivariable logistic regression analyses suggested that high viral load, rece
169                                   Results of regression analyses suggested that the TRQ of SMTs impro
170 37) and control (n=34) groups using logistic regression analyses that included gender, age and diseas
171                             In multivariable regression analyses, the adjusted treatment effects rema
172 adherence rates were compared; multivariable regression analyses then examined and controlled for oth
173                        We performed logistic regression analyses to assess associations between frail
174  interrupted time series logistic or ordinal regression analyses to assess changes in prevalence of s
175                                      We used regression analyses to assess changes in the number of i
176             We performed univariate logistic regression analyses to assess the association between ou
177 ears earlier, using univariable and multiple regression analyses to assess the associations between p
178                      We performed univariate regression analyses to assess the relationship between h
179  included as covariates in multiple logistic regression analyses to calculate adjusted ORs.
180 gene mutation; (ii) weighted ordinary linear regression analyses to compare BFMMS and BFMDS outcomes
181 d Medicare, we conducted multilevel logistic regression analyses to compare chronic opioid use (>= 90
182               We performed adjusted logistic regression analyses to compare early (0-4 days) and late
183 etric mapping 12-based, voxel-wise, multiple-regression analyses to detect white matter hyperintense
184    We performed multiple linear and logistic regression analyses to determine whether HIV/HCV mono- o
185                 We used conditional logistic regression analyses to estimate odds ratios for maternal
186                         We used multivariate regression analyses to estimate the effects of AIT, adju
187                             We used logistic regression analyses to estimate the strength of associat
188            We conducted unadjusted segmented regression analyses to examine temporal trends in HAT ad
189                    We carried out additional regression analyses to explore patterns in case-fatality
190               We performed multivariable Cox regression analyses to identify factors associated with
191 istic regression and Cox proportional hazard regression analyses to identify potential risk factors a
192                                      We used regression analyses to identify which factors were assoc
193           We used multiple variable logistic regression analyses to investigate factors associated wi
194 te and bivariate) and multivariable logistic regression analyses to longitudinal health insurance enr
195                             We used logistic regression analyses to model multivariate associations,
196 n to link the results from the multivariable regression analyses to the qualitative findings.
197 aca maura), we used permutation-based linear regression analyses to understand how life history and s
198                 First, results of unadjusted regression analyses using the entire sample showed the g
199 risk differences were obtained from logistic regression analyses using the predicted marginal approac
200  We compared two sets of multilevel logistic regression analyses, using (a) individual level exposure
201                                    We employ regression analyses varying model specifications and mea
202                            In unadjusted Cox regressions analyses, very low BMD was association with
203                                          Cox regression analyses was used to calculate univariate and
204                                  In adjusted regression analyses, we examined associations of brain i
205                               Using multiple regression analyses, we found that brain response in the
206                                  In logistic regression analyses, we found that having had an ICD sho
207                                     Logistic regression analyses were adjusted for surgical factors a
208         Univariate and multivariate logistic regression analyses were applied to calculate odds ratio
209                       Multivariable logistic regression analyses were applied to determine which base
210                     Cox proportional hazards regression analyses were conducted between imaging metri
211                                              Regression analyses were conducted for AEA and 2-AG on T
212 e analyses and multivariable binary logistic regression analyses were conducted on weighted data.
213              Multivariable weighted logistic regression analyses were conducted to determine physicia
214                                       Linear regression analyses were conducted to determine whether
215                                 Survival and regression analyses were conducted to evaluate the outco
216                         Multivariable linear regression analyses were conducted to explore the associ
217                         Multinomial logistic regression analyses were conducted to identify associati
218                   Subgroup analyses and meta-regression analyses were conducted to identify etiologic
219 ecific univariate and multivariable logistic regression analyses were conducted.
220 scriptive statistics and linear multivariate regression analyses were conducted.
221                       Multivariable logistic regression analyses were conducted.
222                            Subgroup and meta-regression analyses were conducted.
223 effects meta-analyses and mixed-effects meta-regression analyses were done to assess associations bet
224 Bivariable and multivariable binary logistic regression analyses were done.
225                                          Cox regression analyses were employed to evaluate associatio
226                                     LD score regression analyses were first used to estimate the gene
227                                     Logistic regression analyses were performed and adjusted for seve
228                              Multiple linear regression analyses were performed for 12 cortical and s
229                Descriptive and multivariable regression analyses were performed for 3 ocular health c
230        Multivariate Cox proportional hazards regression analyses were performed for ages 0 to 60 and
231    Kaplan-Meier and Cox proportional hazards regression analyses were performed for survival analysis
232                                 Linear mixed regression analyses were performed for variables with P
233                                     Logistic regression analyses were performed on data from 324 hist
234                          Random-effects meta-regression analyses were performed on general population
235                                Multivariable regression analyses were performed to assess the relatio
236                       Multivariable logistic regression analyses were performed to assess the relatio
237                          Univariate logistic regression analyses were performed to calculate unadjust
238                              Correlation and regression analyses were performed to compare the predic
239       Univariable and multivariable logistic regression analyses were performed to determine the asso
240           Univariate and multivariate linear regression analyses were performed to determine the fact
241                                     Logistic regression analyses were performed to evaluate factors f
242                         Multivariable linear regression analyses were performed to evaluate the assoc
243                  Univariate and multivariate regression analyses were performed to evaluate the assoc
244 equation modeling (SEM), latent hierarchical regression analyses were performed to examine associatio
245                                          Cox regression analyses were performed to examine the associ
246                                          Cox regression analyses were performed to generate a weighte
247                                     Logistic regression analyses were performed to generate odds rati
248 (LASSO)-penalized and multivariable logistic regression analyses were performed to identify clinical,
249       Univariable and multivariable logistic regression analyses were performed to identify parameter
250              Univariate and multivariate Cox regression analyses were performed to identify parameter
251 regression tree (CART) analysis and logistic regression analyses were performed to identify protein c
252                      Univariate and multiple regression analyses were performed to identify the pretr
253                Univariable and multivariable regression analyses were performed to identify variables
254                                       Linear regression analyses were performed with either TBR or CT
255                Univariate tests and logistic regression analyses were performed, studying the effects
256                    Linear and logistic mixed regression analyses were performed, with study site and
257 -test, Fischer exact test, and multivariable regression analyses were performed.
258                           Trend and logistic regression analyses were performed.
259 logistic (binary and multinomial) and linear regression analyses were performed.
260            Univariable and multivariable Cox regression analyses were performed.
261                                          Cox regression analyses were performed.
262                Univariable and multivariable regression analyses were performed.
263 mixed model for repeated measures and linear regression analyses were performed.
264              Uni- and multivariable logistic regression analyses were performed.
265 , multivariate, and Cox proportional hazards regression analyses were performed.Measurements and Main
266        Random-effects meta-analyses and meta-regression analyses were undertaken.
267                        Multivariate logistic regression analyses were undertaken.
268                       Multivariable logistic regression analyses were undertaken.
269                  Univariate and multivariate regression analyses were used to assess differences in e
270                         Multivariable linear regression analyses were used to assess differences in m
271                Multivariable binary logistic regression analyses were used to assess predictor of upg
272                       Multivariable logistic regression analyses were used to assess the association
273 onal study, multivariate linear and logistic regression analyses were used to assess the correlation
274                                              Regression analyses were used to assess whether PRS pred
275                 Chi-squared and multivariate regression analyses were used to compare frequencies and
276 an-Meier curves and Cox proportional hazards regression analyses were used to compare OS of patients
277                Multivariate semi-logarithmic regression analyses were used to determine correlations.
278                     Cox proportional hazards regression analyses were used to estimate associations w
279                                          Cox regression analyses were used to estimate crude and adju
280                                          Cox regression analyses were used to estimate the risk of fi
281                                     Multiple regression analyses were used to evaluate associations w
282        Multivariable Cox proportional hazard regression analyses were used to evaluate treatment-asso
283                                     Logistic regression analyses were used to explore associations be
284                                     Logistic regression analyses were used to explore the association
285         Univariate and multivariate logistic regression analyses were used to identify demographic, t
286                                     Logistic regression analyses were used to identify determinants a
287                  Univariate and multivariate regression analyses were used to identify independent pr
288         Univariate and multivariate logistic regression analyses were used to identify predictors of
289                                              Regression analyses were used to identify relationships
290                       Competing risk and Cox regression analyses were used to investigate the associa
291                              Stepwise linear regression analyses were used to investigate the contrib
292                         Kaplan-Meier and Cox regression analyses were used.
293                       Multivariable logistic regression analyses were used.
294  typically designed for group comparisons or regression analyses, which do not utilize temporal infor
295 d for the entire lung, and multiple logistic regression analyses with areas under the curve (AUCs) as
296                                 We conducted regression analyses with LGMM subgroups as predictors of
297                                However, meta-regression analyses with moderators were significant whe
298 frailty and ascites or HE and competing risk regression analyses (with liver transplantation as the c
299  regression analyses and polytomous logistic regression analyses (with one control set and multiple c
300                                  Whole brain regression analyses within the PD group identified QSM i

 
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