1 GRSs with DR were determined using logistic
regression analyses.
2 re estimated using Kaplan-Meier and logistic
regression analyses.
3 l versus TMJOA status in multilevel logistic
regression analyses.
4 n-Meier curves, and Cox proportional hazards
regression analyses.
5 volume change were investigated with linear
regression analyses.
6 al analysis, we used Bland-Altman and linear
regression analyses.
7 xamined by single-marker and multimarker Cox
regression analyses.
8 ere determined using Cox proportional hazard
regression analyses.
9 -related outcomes were evaluated with linear
regression analyses.
10 ular, and LA strain measures was assessed by
regression analyses.
11 ity were assessed using multinomial logistic
regression analyses.
12 eir association with melanoma using logistic
regression analyses.
13 hy and diabetes mellitus were compared using
regression analyses.
14 k factors for infectious complications using
regression analyses.
15 ratios were calculated in log-linear Poisson
regression analyses.
16 were identified using logistic multivariable
regression analyses.
17 lacune volume, and brain volume) in multiple
regression analyses.
18 were examined using Cox proportional hazards
regression analyses.
19 rneal thickness measurements by multivariate
regression analyses.
20 stributions that can be obscured by standard
regression analyses.
21 rrelated and linearly associated with age in
regression analyses.
22 ion and self-harm at 18 years using logistic
regression analyses.
23 e, GHGs, and dietary costs were evaluated in
regression analyses.
24 loss (DCGL) were examined using adjusted Cox
regression analyses.
25 d for between-group comparisons and multiple
regression analyses.
26 d trial (MAKI), using adjusted multivariable
regression analyses.
27 e used to conduct fixed-effects longitudinal
regression analyses.
28 l outcomes was explored with correlation and
regression analyses.
29 were evaluated using multivariable logistic
regression analyses.
30 lar death was assessed using Cox and Poisson
regression analyses.
31 on before breast cancer in multivariable Cox
regression analyses.
32 n analyses and for overall survival with Cox
regression analyses.
33 ity score) to control for confounders in Cox
regression analyses.
34 of MR imaging abnormalities by using linear
regression analyses.
35 ncer were assessed in multivariable logistic
regression analyses.
36 trix regression) and mass-univariate (linear
regression) analyses.
37 s of structural equation models and multiple
regression analyses; (
2) genetic/environmental effects o
38 justment for potential confounders in linear
regression analyses,
a higher aMED was significantly ass
39 Cox
regression analyses adjusted for age and gender showed t
40 The multivariable
regression analyses adjusted for age, gender, best-corre
41 Differences were assessed with multivariable
regression analyses adjusted for age, sex, body mass ind
42 We conducted conditional logistic
regression analyses adjusted for body mass index, smokin
43 [QTc]) were evaluated by using multivariable
regression analyses adjusted for demographic data, risk
44 By use of
regression analyses adjusted for demographics, gross and
45 In linear
regression analyses adjusted for maternal age, race/ethn
46 ions were explored using multivariate linear
regression analyses adjusted for potential confounders.
47 Regression analyses (
adjusted for year of birth, sex, th
48 In multivariate logistic
regression analyses adjusting by age, gender, anti-diabe
49 Univariate and multivariable
regression analyses adjusting for demographics, cardiova
50 atopic diseases were examined using logistic
regression analyses adjusting for potential confounders.
51 Cox multivariate
regression analyses adjusting for recipient and donor tr
52 vertexwise analyses in FreeSurfer and linear
regression analyses adjusting for relevant covariates us
53 We performed Cox proportional hazards
regression analyses,
adjusting for demographic character
54 urvival was analyzed using multivariable Cox
regression analyses,
adjusting for diagnosis year, refer
55 d between groups using multivariate logistic
regression analyses,
adjusting for maternal age, ethnici
56 We performed logistic
regression analyses,
adjusting for maternal and sibling
57 We did logistic
regression analyses,
adjusting for relevant factors, to
58 This difference remained significant on
regression analyses after control for confounders.
59 e and multivariable Cox proportional hazards
regression analyses among participants with ATTR cardiac
60 For the plasma samples, Deming
regression analyses and Bland-Altman plots showed excell
61 tion were examined with multivariable linear
regression analyses and cross-lagged modeling.
62 Quantitative
regression analyses and exposure assessment guidance wer
63 es was evaluated with multivariable logistic
regression analyses and for overall survival with Cox re
64 The
regression analyses and growth mixture models used robus
65 ent characteristics on fatigue with multiple
regression analyses and identified fatigue trajectories
66 he GRSs were examined with the use of linear
regression analyses and meta-analyses.
67 Conventional
regression analyses and MSMs produced similar estimates,
68 Multiple
regression analyses and structural equation models were
69 tial infarct location in simple and multiple
regression analyses and using voxel-based lesion-symptom
70 sets using Kaplan-Meier and multivariate Cox
regression analyses and was further validated in 42 prim
71 T testing, ANOVA, and
regression analyses are reviewed.
72 Regression analyses assessing the association between Te
73 Logistic
regression analyses based on a conceptual model of DR ri
74 Multivariable logistic
regression analyses based on Andersen's Behavioral Model
75 Linear
regression analyses before and after multivariable adjus
76 We conducted conventional linear
regression analyses,
both unadjusted and adjusted for ti
77 edits and health outcomes using conventional
regression analyses,
but they did not account for time-v
78 Single and multiple mixed-effect logistic
regression analyses,
chi(2) tests, and Bonferroni correc
79 In propensity-adjusted
regression analyses,
clinical new-onset atrial fibrillat
80 Linear mixed effects (
regression) analyses conducted separately for the depend
81 Cox
regression analyses controlling for baseline depressive
82 In multivariate
regression analyses,
controlling for possible confoundin
83 Based on multivariable Cox
regression analyses,
cytogenetic abnormalities and mutat
84 ariate Cox proportional hazards and logistic
regression analyses demonstrated consistent significance
85 Results Multiple linear
regression analyses demonstrated significant association
86 Also,
regression analyses demonstrated that the variables peri
87 ample size and characteristics varied across
regression analyses,
depending on mutual information ava
88 In multivariable conditional logistic
regression analyses,
diabetes mellitus; higher body mass
89 In multivariable
regression analyses,
each 1-point increase in the DHAKA
90 In unadjusted and adjusted conventional
regression analyses,
each additional year of receiving t
91 On multiple linear
regression analyses,
ECV independently predicted intrins
92 design, multivariable unconditional logistic
regression analyses estimated odds ratios and 95% CIs fo
93 Logistic
regression analyses examined the association between pre
94 Logistic
regression analyses examined the number of past-year sui
95 ts in the Framingham Heart Study, performing
regression analyses for each protein versus each clinica
96 We used multivariable Cox
regression analyses for incident diabetes (892 new cases
97 Regression analyses found maternal Fe status was signifi
98 Using multivariate logistic
regression analyses four SNPs were significantly associa
99 Hierarchical
regression analyses further show that variations in spat
100 In multivariable
regression analyses,
greater postoperative angle widenin
101 In the multivariable
regression analyses,
higher circulating adiponectin was
102 In multivariable-adjusted Cox
regression analyses,
ID associated with increased mortal
103 Regression analyses in humans (n=259 796) identified the
104 r postoperatively were developed by logistic
regression analyses in the Finnish patient cohort.
105 identified as independently important in our
regression analyses included cesarean-section delivery,
106 Covariates for
regression analyses included sex, age, medical school co
107 from age- and race/ethnicity-adjusted linear
regression analyses indicated modest, but statistically
108 Our
regression analyses indicated no racial disparities in O
109 Results from multivariable linear
regression analyses indicated that serum concentrations
110 sion repeatability were assessed with linear
regression analyses,
intraclass correlation coefficients
111 Multivariable Cox
regression analyses investigated the effect of the timin
112 endall tau correlation, multivariable linear
regression analyses,
Kruskal-Wallis rank sum test, and p
113 result after 1 year.In univariable logistic
regression analyses laparoscopic surgery and male sex pr
114 In Cox
regression analyses,
larger CTG expansions were signific
115 fit these data best, suggesting that common
regression analyses likely conceal substantial interindi
116 We used multiple
regression analyses (
logistic or binominal) to compare t
117 In meta-
regression analyses,
mean illness duration was positivel
118 In
regression analyses,
models comprising significant varia
119 In age-adjusted Cox
regression analyses,
neprilysin concentrations were sign
120 , we replicated previously reported LD score
regression analyses of 49 traits/diseases using LD Hub;
121 Linear
regression analyses of 6-month recipient renal function
122 In the
regression analyses of manual versus semiautomated volum
123 , we used pfhrp2/3-specific PCR and logistic
regression analyses of potentially associated epidemiolo
124 tern of results was observed in the multiple
regression analyses of wave 2 prevalent psychiatric diso
125 s missing data and performed binary logistic
regression analyses on complete-case and imputed dataset
126 In Cox proportional hazards
regression analyses,
only age (P = .02), sex (P = .01),
127 In univariate and multivariate Cox
regression analyses,
only female recipient was associate
128 ean and modelled each outcome using logistic
regression analyses,
overall and stratified by child sex
129 In univariate
regression analyses,
patient age, advanced cataract, jun
130 In Cox
regression analyses,
patients with NNAs at screening had
131 use mortality using Cox proportional hazards
regression analyses,
performed in 2015.
132 Regression analyses related network connectivity to over
133 Regression analyses related these maps to behavioral inh
134 Both univariate and multiple linear
regression analyses reported that the models could expla
135 patients were identified by logistic and Cox
regression analyses,
respectively.
136 Logistic
regression analyses revealed a strong/independent associ
137 Multivariate multiple
regression analyses revealed decreased fractional anisot
138 Regression analyses revealed significant effects of age
139 Multivariate Cox
regression analyses revealed that GPS, NLR, and occurren
140 Meta-
regression analyses revealed that greater scores on meas
141 Subgroup and meta-
regression analyses revealed that medication use, medica
142 Multiple
regression analyses revealed that mothers' defense mecha
143 Regression analyses revealed that this combination of fa
144 Whole-brain
regression analyses revealed that trait self-esteem was
145 Multivariable logistic
regression analyses revealed that, in contrast to the ad
146 Univariate
regression analyses showed a significant positive associ
147 Multivariate
regression analyses showed a significant positive associ
148 Regression analyses showed age, disease risk, and donor
149 Negative binomial
regression analyses showed an association between prenat
150 Regression analyses showed little difference in odds rat
151 Meta-
regression analyses showed relative risk reductions prop
152 Furthermore, meta-
regression analyses showed that age, gender and sample s
153 Multiple logistic-
regression analyses showed that DNI was a predictive fac
154 Regression analyses showed that each additional word or
155 Regression analyses showed that elevated 1-month hsCRP w
156 Piecewise
regression analyses showed that levels of salivary cotin
157 Univariate meta-
regression analyses showed that the major sources of het
158 Multiple
regression analyses showed that the strongest predictor
159 Adjusted multivariate
regression analyses showed that, compared with mothers w
160 Regression analyses showed that, on both tasks, the more
161 Using stepwise linear
regression analyses,
significant associations were ident
162 a significant moderator in subgroup and meta-
regression analyses (
slope beta = -0.16; 95% CI, -0.29 t
163 ocrine therapy, we used Kaplan-Meier and Cox
regression analyses,
stratified according to trial and t
164 Bivariable logistic
regression analyses suggested that high viral load, rece
165 After uni- and multivariate logistic
regression analyses,
surgery by ELAPE remained a risk fa
166 In adjusted Cox
regression analyses,
SYNTAX score and diabetes mellitus
167 Regression analyses tested whether attention, executive
168 In univariate and multivariate logistic
regression analyses the algorithm (i.e., DILI score mode
169 In separate Cox
regression analyses,
the MRI-derived left ventricular en
170 We performed univariate logistic
regression analyses to assess the association between ou
171 Regression analyses to assess the association of NEI VFQ
172 We used mixed effects linear and logistic
regression analyses to assess whether psychological traj
173 We used logistic
regression analyses to calculate odds ratios (and 95% co
174 We used multivariable logistic
regression analyses to describe risk factors associated
175 We used logistic
regression analyses to determine the association between
176 We used conditional logistic
regression analyses to estimate odds ratios for maternal
177 We performed logistic
regression analyses to estimate the association between
178 We used logistic
regression analyses to estimate the strength of associat
179 We used Cox
regression analyses to examine the association between b
180 We used different tests and multivariate
regression analyses to examine the cohort differences.
181 es Saved Tool (LiST) and did multiple linear
regression analyses to explain the reduction in child mo
182 We used imputation and conditional logistic
regression analyses to fine-map the associations.
183 We performed multivariable Cox
regression analyses to identify factors associated with
184 We used multivariable logistic
regression analyses to identify factors associated with
185 stage breast cancer (stage III/IV), and meta-
regression analyses to identify potential sources of var
186 We used multivariable logistic
regression analyses to identify predictors of PAH.
187 We then used logistic
regression analyses to identify preoperative factors ass
188 rformed univariate and multivariate logistic
regression analyses to identify variables associated wit
189 te and bivariate) and multivariable logistic
regression analyses to longitudinal health insurance enr
190 We used mixed effects multiple
regression analyses to relate each preoperative VFT to u
191 We used bivariable and logistic
regression analyses to study the association of PPCs wit
192 We did
regression analyses to validate the DHAKA score and comp
193 Using novel nonlinear
regression analyses (
two-moment regression), we illustra
194 We performed multivariable logistic
regression analyses,
using the generalized estimating eq
195 coefficient (ICC) assessed with mixed-model
regression analyses was the metric for interreader relia
196 Cox
regression analyses was used to calculate univariate and
197 Using
regression analyses,
we compared the proportion of varia
198 Using partial
regression analyses,
we find that studies that ignore th
199 Notably, in Cox
regression analyses,
we found no association of efflux c
200 The
regression analyses were adjusted for age, sex, calendar
201 r of prespecified subgroup analyses and meta-
regression analyses were also done.
202 Multivariable logistic
regression analyses were applied to determine which base
203 oreover, Spearman's correlation and multiple
regression analyses were carried out.
204 eier estimates, and Cox proportional hazards
regression analyses were completed to evaluate risk fact
205 Cox proportional hazards
regression analyses were conducted between imaging metri
206 Linear
regression analyses were conducted between the following
207 Statistical
regression analyses were conducted to correlate the soil
208 McNemar comparisons and logistic
regression analyses were conducted to evaluate covariate
209 Univariate and multivariable logistic
regression analyses were conducted to evaluate potential
210 Correlation and
regression analyses were conducted to examine P50 suppre
211 A series of bivariate
regression analyses were conducted to examine the associ
212 Multivariable linear
regression analyses were conducted to explore the associ
213 Multilevel and
regression analyses were conducted.
214 utcomes, and sensitivity, subgroup, and meta-
regression analyses were conducted.
215 Multivariable logistic
regression analyses were conducted.
216 Subgroup and meta-
regression analyses were conducted.
217 Descriptive and
regression analyses were done to examine associations be
218 Correlation and
regression analyses were performed among airway pressure
219 Bivariate and mixed-effects
regression analyses were performed assessing factors ass
220 Regression analyses were performed between gray matter c
221 Among eyes with an abnormal 10-2 VF,
regression analyses were performed between the Amsler gr
222 Descriptive and multivariable logistic
regression analyses were performed for 3 ocular health c
223 Descriptive and multivariable
regression analyses were performed for 3 ocular health c
224 Multiple
regression analyses were performed for the association b
225 istry, Kaplan-Meier, competing risk, and Cox
regression analyses were performed on adult, first kidne
226 chi(2) tests for trend and logistic
regression analyses were performed on the data.
227 Multivariable Cox
regression analyses were performed to assess differences
228 Multiple linear
regression analyses were performed to assess interventio
229 Multivariable modified Poisson
regression analyses were performed to assess the effect
230 Multivariable modified Poisson
regression analyses were performed to assess the effect
231 Multivariable
regression analyses were performed to assess the relatio
232 Cox
regression analyses were performed to correlate both bio
233 Linear
regression analyses were performed to determine associat
234 Kaplan-Meier and Cox
regression analyses were performed to determine lymphede
235 Univariate and multivariate
regression analyses were performed to determine the asso
236 Univariate and multivariate linear
regression analyses were performed to determine the fact
237 Multivariable logistic and Cox
regression analyses were performed to determine the inde
238 characteristic curves and stepwise logistic
regression analyses were performed to determine the opti
239 Spearman correlation and multiple
regression analyses were performed to determine the rela
240 Multivariable
regression analyses were performed to develop models for
241 Logistic
regression analyses were performed to evaluate factors f
242 Cox
regression analyses were performed to evaluate whether P
243 Binary logistic
regression analyses were performed to identify factors a
244 Simple and multivariate logistic
regression analyses were performed to identify independe
245 regression tree (CART) analysis and logistic
regression analyses were performed to identify protein c
246 Uni- and multivariate logistic
regression analyses were performed to identify risk fact
247 Logistic
regression analyses were performed to identify the assoc
248 Logistic
regression analyses were performed to investigate which
249 Linear and logistic
regression analyses were performed to test study hypothe
250 Descriptive statistics and logistic
regression analyses were performed, and all analyses wer
251 Multivariate Cox
regression analyses were performed, censoring at cardiac
252 Univariate tests and logistic
regression analyses were performed, studying the effects
253 Univariable and multiple logistic
regression analyses were performed, using multiple imput
254 AFLD, univariable and multivariable logistic
regression analyses were performed, with high-risk plaqu
255 Descriptive statistics, t tests, and
regression analyses were performed.
256 Kaplan-Meier and Cox
regression analyses were performed.
257 Multiple linear
regression analyses were performed.
258 Multivariable linear and logistic
regression analyses were performed.
259 Propensity score and
regression analyses were performed.
260 Mixed-model
regression analyses were performed.
261 Cox
regression analyses were performed.
262 Univariable and multivariable
regression analyses were performed.
263 , paired and multiple group comparisons, and
regression analyses were performed.
264 active female students (n = 2288); logistic
regression analyses were restricted to sexually active f
265 and mental health problems, binary logistic
regression analyses were run.
266 Regression analyses were undertaken to identify the best
267 Multivariable logistic
regression analyses were undertaken.
268 with and without MLNR were compared and Cox
regression analyses were used to adjust for demographic,
269 an-Meier curves and Cox proportional hazards
regression analyses were used to compare OS of patients
270 (IPTW) -adjusted Kaplan-Meier curves and Cox
regression analyses were used to compare OS of patients
271 Multivariable difference-in-difference
regression analyses were used to compare states with Med
272 Multivariate semi-logarithmic
regression analyses were used to determine correlations.
273 Univariate and multivariable logistic
regression analyses were used to determine factors assoc
274 Cox
regression analyses were used to determine variables ass
275 Logic and logistic
regression analyses were used to develop a model for the
276 variable forward selection stepwise logistic
regression analyses were used to develop predictive mode
277 Extended Cox
regression analyses were used to estimate hazards of exp
278 Logistic
regression analyses were used to estimate the associatio
279 Logistic and Cox
regression analyses were used to evaluate perioperative
280 Uni- and multivariable Cox
regression analyses were used to evaluate the associatio
281 Multivariable Cox proportional hazard
regression analyses were used to evaluate treatment-asso
282 Hierarchical
regression analyses were used to examine the factors ass
283 Regression analyses were used to identify associations b
284 Logistic
regression analyses were used to identify determinants a
285 Univariate and multivariate
regression analyses were used to identify independent pr
286 The Kaplan-Meier method and Cox
regression analyses were used to identify predictors of
287 Multivariable
regression analyses were used to identify predictors.
288 Cox
regression analyses were used to investigate prospective
289 Multivariable linear and logistic
regression analyses were used to investigate the associa
290 Cox proportional hazard
regression analyses were used to investigate the risk of
291 Multivariate
regression analyses were used to study associations of h
292 makeup, and early environmental factors, Cox
regression analyses were used, conditioning on individua
293 tiple linear regression, as well as quantile
regression, analyses were performed to investigate the r
294 Regressions analyses were performed using Cox regression
295 an additional 7 were also significant in Cox
regression analyses when adjusted for age, sex, and N-te
296 typically designed for group comparisons or
regression analyses,
which do not utilize temporal infor
297 We used multivariable logistic
regression analyses with 3-level hierarchical adjustment
298 gated using interrupted time series logistic
regression analyses with adjustment for confounders.
299 Multivariate linear
regression analyses with generalized estimating equation
300 However, meta-
regression analyses with moderators were significant whe