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1 to account for between-trial differences in baseline characteristics.
2 sed instead of multivariable adjustments for baseline characteristics.
3 s were used to compensate for differences in baseline characteristics.
4 sulted in 355 pairs of patients with similar baseline characteristics.
5 The groups had similar baseline characteristics.
6 for significant between-group differences in baseline characteristics.
7 (n = 696) patients with DLBCL had comparable baseline characteristics.
8 g was used to assemble a cohort with similar baseline characteristics.
9 sed to select subpopulations with comparable baseline characteristics.
10 o assemble a cohort of patients with similar baseline characteristics.
11 was applied to select 2 groups with similar baseline characteristics.
12 re used to compensate for the differences in baseline characteristics.
13 Both groups were similar regarding baseline characteristics.
14 g were used to compensate for differences in baseline characteristics.
15 was performed to account for differences in baseline characteristics.
16 neither target, adjusting for differences in baseline characteristics.
17 s (PR) were adjusted for cluster effects and baseline characteristics.
18 e used to compensate for differences in some baseline characteristics.
19 The groups were well matched for baseline characteristics.
20 t significantly altered after adjustment for baseline characteristics.
21 ality incrementally and independently of all baseline characteristics.
22 eronegative participants while adjusting for baseline characteristics.
23 score matching to control for differences in baseline characteristics.
24 log-linear mixed effects models adjusted for baseline characteristics.
25 sible due to differences in study design and baseline characteristics.
26 ensity score matching was used to adjust for baseline characteristics.
27 e <70 versus >/=70 beats/min, balanced on 58 baseline characteristics.
28 ving and not receiving CCBs, balanced on 114 baseline characteristics.
29 They were matched for 6 baseline characteristics.
30 n matched 1:1 based on propensity scoring of baseline characteristics.
31 nsity within 28 days in an adjusted model of baseline characteristics.
32 The 2 groups were well-matched in terms of baseline characteristics.
33 The 2 groups were well balanced in terms of baseline characteristics.
34 ching was used to correct for differences in baseline characteristics.
35 black and white patients after adjusting for baseline characteristics.
36 t patients significantly after adjusting for baseline characteristics.
37 except sudden death in a model including all baseline characteristics.
40 atment groups may not be comparable in their baseline characteristics, a phenomenon known as confound
41 population with no significant difference in baseline characteristics (age, 79+/-8.2 years; 60% women
43 chical Cox modeling to adjust for imbalanced baseline characteristics, allocation to dexmedetomidine
44 t weighting was used to balance more than 60 baseline characteristics among patients in the 4 drug co
46 There were no significant differences in baseline characteristics among the 4 groups, thus confir
47 cant differences in patient demographics and baseline characteristics among the 4 treatment groups.
48 ciation between CTG expansion size and other baseline characteristics and (1) cardiac involvement at
49 models that accounted for variations in (1) baseline characteristics and (2) both baseline and surgi
52 ncluded to evaluate selection criteria used, baseline characteristics and clinical outcomes at 3-mont
54 subgroups that differed significantly in all baseline characteristics and clinical trajectories, desp
56 ional hazards regression models adjusted for baseline characteristics and drugs to investigate the as
66 nd identification of the association between baseline characteristics and occurence structural progre
69 bstracts for observational studies comparing baseline characteristics and outcomes in patients with N
70 linked to Medicare was used to determine the baseline characteristics and outcomes of 9093 patients w
73 allic DES and could be determined by patient baseline characteristics and periprocedural factors.
75 D compared with those without, adjusting for baseline characteristics and postprocedure medications.
76 ective and in-hospital time horizon; derived baseline characteristics and probabilities of intensive
80 ed by joinpoint regression were compared for baseline characteristics and resource use, including tot
81 njections administered during the OLE versus baseline characteristics and response to treatment durin
84 ssion was used to study associations between baseline characteristics and the occurrence of structura
86 an initial risk group on the basis of their baseline characteristics and then underwent 32 days of i
90 usted for calendar time, admitting hospital, baseline characteristics, and in-hospital revascularizat
104 count for observed systematic differences in baseline characteristics between patients treated and th
105 alysis was used to adjust for differences in baseline characteristics between patients with and witho
113 ska Native patients; but after adjusting for baseline characteristics, black race and Hispanic ethnic
117 8 was higher in subgroups with the following baseline characteristics compared with the complementary
129 trations of 500 pg/mL or higher, we compared baseline characteristics, duration of heparin treatment,
131 fficacy International Trial and adjusted for baseline characteristics during 6 years' median follow-u
132 elected from a bank of messages according to baseline characteristics (eg, smoking) and delivered via
134 ase data for rates of STEMI, treatments, and baseline characteristics from patients attending each sa
137 djusted life years according to all assessed baseline characteristics (ICU admission after elective s
139 gy Diary users were able to properly provide baseline characteristics (ii) simple phenotypic characte
141 bleeding in the 1-year mortality model with baseline characteristics improved it to an extent compar
146 no significant differences between groups in baseline characteristics including age, sex, site of inf
152 for EDS was generated, which included seven baseline characteristics, including non-motor symptoms a
153 d preadmission diuretic therapy, traditional baseline characteristics, including right heart catheter
157 th those undergoing optical keratoplasty for baseline characteristics, management details, and visual
158 ce-eligible patients who were balanced on 28 baseline characteristics (mean age, 79 years; 58% women;
160 ltivariate regression models including urban baseline characteristics, meteorological variables, and
163 However, when accounting for differences in baseline characteristics, multiple fractures, and/or eve
164 attern remained similar after adjustment for baseline characteristics (odds ratio in the SIRS-positiv
165 There were no between-group differences in baseline characteristics of 1,489 eligible patients with
167 w-up through December 31, 2015, compared the baseline characteristics of AYA (13-39 years old) and OA
171 s on protein structure and function, and the baseline characteristics of indels in 2504 individuals o
178 ysis and network-based statistics to explore baseline characteristics of patients who subsequently re
193 roportion of patients, and did not relate to baseline characteristics or disease progression, but add
203 were performed to adjust for differences in baseline characteristics, procedural indications, and co
207 tigators to share individual patient data on baseline characteristics, stroke severity and type, end-
208 2 factorial design minimizing differences in baseline characteristics such as age, sex, and duration
209 Nut consumption was associated with some baseline characteristics such as lower body mass index a
210 greater impact on NO2 and PM2.5 change than baseline characteristics, suggesting urban design and la
211 d December 2005, 9792 patients with complete baseline characteristics, surgery procedure, and follow-
217 The study groups were similar with regard to baseline characteristics; the median age of the patients
218 confounders such as MS relapses, as well as baseline characteristics, through the use of inverse pro
219 s were observed between them with respect to baseline characteristics, thus emphasizing the comparabi
222 r agents from 2000 to 2008 were analyzed for baseline characteristics, toxicities, responses, and sur
224 propensity matching, the only difference in baseline characteristics was the use of neoadjuvant chem
241 sed the risk of transformation or death when baseline characteristics were considered with a hazard r
246 servational studies that did not control for baseline characteristics were excluded, catheter-related
251 phics, clinical characteristics, and imaging baseline characteristics were presented by descriptive s
276 ts with known IHD (18% women, 82% men), most baseline characteristics were similar between women and
293 Despite variation in indications for GPIs, baseline characteristics were well balanced between the
296 mpared with matched patients with comparable baseline characteristics who underwent atrial fibrillati
297 o identify 68 triads of patients matched for baseline characteristics who were treated with early ant
298 nalysis was used to determine association of baseline characteristics with multidrug resistance.
299 we extracted the most recent measurement of baseline characteristics (within 2 years of diagnosis) f
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