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