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1 umab, P = 0.20; global P = 0.09 adjusted for potential confounders).
2 e studies should address the problem of this potential confounder.
3 d infant mortality rates with adjustment for potential confounders.
4 by low-dose aspirin use after adjusting for potential confounders.
5 5% confidence intervals while accounting for potential confounders.
6 nts and AC- participants after adjusting for potential confounders.
7 ognitive deficits are robust or explained by potential confounders.
8 adults without MDD after adjustment for many potential confounders.
9 ultivariable linear regression, adjusted for potential confounders.
10 atistically significant after adjustment for potential confounders.
11 WAZ changes (P = 0.103) after adjustment for potential confounders.
12 io, 1.34 [1.01-1.79]; p < 0.05) adjusted for potential confounders.
13 AF with subsequent cancer and to adjust for potential confounders.
14 s stratified by matched set and adjusted for potential confounders.
15 iables, adjusting for matching variables and potential confounders.
16 geQTL can also correct the effects of potential confounders.
17 variable logistic regression controlling for potential confounders.
18 95% CI, 2.39-138.22; P = .005) adjusting for potential confounders.
19 multivariable linear regression adjusted for potential confounders.
20 adiographs (P = 0.005), when controlling for potential confounders.
21 31) disappeared after further adjustment for potential confounders.
22 CI, 2.00-4.17; P < .001) after adjusting for potential confounders.
23 P = .001) quadrants even after adjusting for potential confounders.
24 portional hazard models, with adjustment for potential confounders.
25 1.981; 95% CI = 1.246-3.149), adjusting for potential confounders.
26 for 52 weeks with and without adjustment for potential confounders.
27 R; 2.66; 95% CI: 1.53, 4.61), independent of potential confounders.
28 ignificant after multivariate adjustment for potential confounders.
29 x, and period of interview, and adjusted for potential confounders.
30 opmental adversities were used to adjust for potential confounders.
31 nal hazards regression model, controlled for potential confounders.
32 rides, high BP, and MetS after adjusting for potential confounders.
33 al logistic regression that was adjusted for potential confounders.
34 ional) and each CHD phenotype, adjusting for potential confounders.
35 onal logistic regression with adjustment for potential confounders.
36 imary exposure indicators along with several potential confounders.
37 ) serum magnesium levels while adjusting for potential confounders.
38 Cox proportional hazards model adjusted for potential confounders.
39 egression analysis was performed to evaluate potential confounders.
40 probabilities regression models adjusted for potential confounders.
41 lifetime exposure to each of these fuels and potential confounders.
42 iate linear regression analyses adjusted for potential confounders.
43 tavirus case-patient status, controlling for potential confounders.
44 e underlying psychiatric disorders and other potential confounders.
45 studies were rarely adequately adjusted for potential confounders.
46 ection for cell type heterogeneity and other potential confounders.
47 gression, adjusting for matching factors and potential confounders.
48 primary HIV risk factor, and cohort site as potential confounders.
49 ad the best performance while accounting for potential confounders.
50 with overall survival while controlling for potential confounders.
51 hips persisted after adjustment for multiple potential confounders.
52 5% confidence intervals (CIs), adjusting for potential confounders.
53 listic simulated data sets with thousands of potential confounders.
54 increased mortality after we controlled for potential confounders.
55 110 mg/dL) after adjustment for clusters and potential confounders.
56 post-transplant survival after adjusting for potential confounders.
57 , adjusting for other outcome predictors and potential confounders.
58 tertiles, respectively), both independent of potential confounders.
59 els adjusted for baseline frailty status and potential confounders.
60 ession analysis was performed to account for potential confounders.
61 nfidence intervals (CIs) with adjustment for potential confounders.
62 l logistic regression analyses to adjust for potential confounders.
63 8]; P < 0.001, respectively), independent of potential confounders.
64 ted using logistic regression to account for potential confounders.
65 tions persisted after adjustment for several potential confounders.
66 stic regression analyses and controlling for potential confounders.
67 of dietary fiber intake, with adjustment for potential confounders.
68 an exposure of interest while adjusting for potential confounders.
69 regression analyses were used to adjust for potential confounders.
70 th cognitive assessment scores, adjusted for potential confounders.
71 icant after adjustment for obesity and other potential confounders.
72 le to the intervention after controlling for potential confounders.
73 y proximity and traffic volume, adjusted for potential confounders.
74 sity score adjustment applied to control for potential confounders.
75 epeated measures analysis and accounting for potential confounders.
76 umes, adjusting for intracranial volumes and potential confounders.
77 ed in Cox regression models and adjusted for potential confounders.
78 A grants persisted even after accounting for potential confounders.
79 ation measures with incident DM adjusted for potential confounders.
80 BMI change, adjusting for batch effects and potential confounders.
81 by using general linear models adjusted for potential confounders.
82 dices and ovarian cancer risk, adjusting for potential confounders.
83 he risk for dementia and AD and adjusted for potential confounders.
84 unit/quartile increase in TDS, adjusting for potential confounders.
85 at different ages and DPOAEs, adjusting for potential confounders.
86 d with BMI, WC, or WHR adjusted for BMI; and potential confounders.
87 2) after adjustment for CVD risk factors and potential confounders.
88 parametric survival models that adjusted for potential confounders.
89 = 16 associated with exposure, adjusting for potential confounders.
90 [95% CI: 1.01 to 1.59]) after adjusting for potential confounders.
91 s with cognitive performance controlling for potential confounders.
92 esistant pathogens, even after adjusting for potential confounders.
93 re used to estimate odds ratios adjusted for potential confounders.
94 tively novel method, to control for numerous potential confounders.
95 ivariable linear regression, controlling for potential confounders.
96 ular sun exposure, even after adjustment for potential confounders.
97 s correlation persisted after adjustment for potential confounders.
98 = .001) after adjustment for a wide range of potential confounders.
99 for demographic, socioeconomic, and clinical potential confounders.
100 y logistic regression analysis adjusting for potential confounders.
101 0.73, 1.00) after adjustment for a range of potential confounders.
102 MVO (beta=0.18; P=0.001) after adjusting for potential confounders.
103 n terms of weight, body mass index, and most potential confounders.
104 in surgical mortality studies to control for potential confounders.
105 istic regression was performed to adjust for potential confounders.
106 d medical conditions, medications, and other potential confounders.
107 g MIG or OG, using year of surgery and other potential confounders.
108 y linear or logistic regression adjusted for potential confounders.
109 logistic, and Cox regression to control for potential confounders.
110 ean population even after adjusting by other potential confounders.
111 f the data and generally limited control for potential confounders.
112 al hazards regression was used to adjust for potential confounders.
113 contraception, breast-cancer diagnoses, and potential confounders.
114 l costs, and financial burden, adjusting for potential confounders.
115 g logistic regression analyses adjusting for potential confounders.
116 confirmed these findings while adjusting for potential confounders.
117 known ADPKD manifestations were adjusted for potential confounders.
118 Analyses were adjusted for potential confounders.
119 ients versus controls, while controlling for potential confounders.
120 ufficient vitamin B-6 status, independent of potential confounders.
121 ated with exposures variables, adjusting for potential confounders.
122 ds regression models were used to adjust for potential confounders.
123 ox proportional hazards models, adjusted for potential confounders.
124 trolling for body mass index (BMI) and other potential confounders.
125 simple linear regression models adjusted for potential confounders.
126 We adjusted analyses for recorded potential confounders.
127 I, 0.58-0.88; P = .002), after adjusting for potential confounders.
128 f ATE was conducted to control for available potential confounders.
129 ent for family history of diabetes and other potential confounders.
130 .90-4.67) after multivariable adjustment for potential confounders.
131 ards regression models, while accounting for potential confounders.
132 ltiple linear regression models adjusted for potential confounders.
133 als, adjusting for tobacco smoking and other potential confounders.
134 ality after adjustment for smoking and other potential confounders (1 cup per day: hazard ratio [HR],
136 lustrate how DAGs can be used to identify 1) potential confounders, 2) mediators and the consequences
139 tratified analyses by sex, after control for potential confounders, a greater GI was linked to a high
142 eprivation on diabetes risk, controlling for potential confounders affecting neighbourhood assignment
143 Analyses were adjusted for the following potential confounders: age, gender, vascular comorbidity
146 T2D was 0.23 (95% CI 0.19-0.29) adjusted for potential confounders and 0.37 (95% CI 0.27-0.50) furthe
147 ations with ART outcomes while adjusting for potential confounders and accounting for repeated treatm
148 o decades of follow-up, after adjustment for potential confounders and allowance for the J-shape asso
149 neighbourhood was modelled as a function of potential confounders and ecological variables, namely:
152 tistically significant after controlling for potential confounders and mediators (hazard ratio=1.38;
154 vival outcomes than repeat PK, adjusting for potential confounders and risk factors for graft failure
155 of the MDS score and AMD, taking account of potential confounders and the multicenter study design.
156 ained elevated after adjustment for relevant potential confounders and was also observed among never-
157 d algorithm used to select a large number of potential confounders) and by comparing exposed children
158 irth risk factors, subset analyses excluding potential confounders, and analyses in preterm and term
159 ilevel linear regression models adjusted for potential confounders, and conducted several sensitivity
160 irected acyclic graphs were used to identify potential confounders, and Cox proportional hazard model
161 neralized linear mixed models, adjusting for potential confounders, and explored effect modification.
162 rol for the outcome-specific associations of potential confounders, and it employs a hierarchical "sh
163 idence interval, 1.0 to 8.0), independent of potential confounders, and tended to develop earlier rec
164 and increased CHD risk was not explained by potential confounders, and there was no evidence of reve
166 ividual level, adjusting for seasonality and potential confounders at individual, clinic, and country
167 on remained significant after adjustment for potential confounders (B=-0.098; 95% confidence interval
168 conducted stratified analyses to analyze the potential confounders behind these discordant outcomes.
173 Following adjustment for smoking and other potential confounders, coffee drinkers, as compared with
174 nd we determined the impact of adjusting for potential confounders collected from a subset of the coh
177 Only a select set of quality indicators and potential confounders could be ascertained from availabl
178 cular outcomes using Cox models adjusted for potential confounders (demographics, clinical characteri
179 Meta-analysis of studies that adjusted for potential confounders demonstrated that preeclampsia was
181 led adjustment of the observational data for potential confounders did not reduce the divergence from
184 ults remained significant when adjusting for potential confounders (e.g., neuropsychological measures
185 In multivariable models, after adjusting for potential confounders, every doubling of GDF-15 level as
186 owing for this association and adjusting for potential confounders, happiness and related measures of
187 risk across all common cancers adjusted for potential confounders has not previously been undertaken
189 ly significant after adjustment for measured potential confounders (HR, 1.19; 95% CI, 1.13-1.24).
190 nuated towards the null after adjustment for potential confounders (HR: 1.22, 95% CI: 0.96, 1.55).
191 sociation that remained after adjustment for potential confounders (HR: 69.5; 95% CI: 7.0, 694.6).
194 ls, stratified on donor sex and adjusted for potential confounders, included a recipient sex by curre
195 .21-0.68; p < 0.01]) in models adjusting for potential confounders including age, initial rhythm, tim
196 iation between PPCS and HRQoL, adjusting for potential confounders including age, sex, prior concussi
197 work and at study enrollment, adjusting for potential confounders including airborne total hydrocarb
199 (-10) , which persisted after adjustment for potential confounders including pathogenic airway bacter
200 of any of these cancers after adjustment for potential confounders including total dietary energy int
202 a HIV cohort study (N = 1261), adjusting for potential confounders, including age, nadir CD4(+) T-cel
204 1.04 to 1.24]; P=0.004) after adjustment for potential confounders, including cardiovascular risk fac
205 Cox proportional hazard models, adjusted for potential confounders, including cardiovascular risk fac
206 estimate associations while controlling for potential confounders, including co-pollutants such as f
208 nsulin-requiring diabetes was independent of potential confounders, including diabetes duration, and
209 ing remained significant after adjusting for potential confounders, including maternal and parental h
217 ase analysis (n = 371 [92.9%]) adjusting for potential confounders, long aggressive regimens were ass
219 insulin concentrations after adjustment for potential confounders [not including body mass index (BM
220 ncreased mortality, even after adjusting for potential confounders (odds ratio [95% CI], 1.14 (1.08-1
222 timate HRs with 95% CIs, with adjustment for potential confounders.Of the 4400 participants, 2551 (57
223 multivariable model was used to control for potential confounders on the length of hospitalization.
224 ing, and cooking with fuels were explored as potential confounders or effect modifiers in logistic re
226 a logistic regression model controlling for potential confounders, ozone exposure was associated wit
235 decreased risk for AMR after adjustment for potential confounders (risk ratio 0.94 per TTV log level
236 ation, lifestyle factors, and morbidities as potential confounders, rLTL was associated with ALM (bet
241 orted fresh fruit consumption, adjusting for potential confounders such as age, sex, region, socio-ec
244 on of biomarkers because of the influence of potential confounders, such as inflammation, alkaline ph
245 ed a propensity score analysis to adjust for potential confounders, such as poorly controlled hyperte
247 esistance, and other dietary and non-dietary potential confounders, the hazard ratio of incident diab
250 the African studies, the paucity of data on potential confounders, the limited statistical power to
253 n a multivariate analysis adjusted for other potential confounders, the probability of being exposed
254 ysis adjusted for admission diagnosis, other potential confounders, the probability of being exposed
256 one loss and with a careful consideration of potential confounders, the risk of a first MI was signif
258 propensity score matching and adjusting for potential confounders, there was no longer a significant
262 zed estimating equations with adjustment for potential confounders to estimate associations between t
263 le logistic regressions, with adjustment for potential confounders, to estimate the associations of n
264 isk of CVD, even after adjusting for several potential confounders (traditional risk factors for CVD,
273 aches that utilize direct adjustment for all potential confounders via regularized regression, includ
276 ion for IVF, other fertility treatments, and potential confounders was collected from medical records
279 ized estimating equation models adjusted for potential confounders, we evaluated the association betw
288 thropometry and atopy at age of 8 years, and potential confounders were available for 1608 participan
289 hout the entire follow-up period, even after potential confounders were controlled for (P < 0.05).
290 in 2002, major chronic conditions, and other potential confounders were controlled for, men with prob
292 ls and without pleiotropic associations with potential confounders were estimated to explain about 0.
294 ltiple linear regression models adjusted for potential confounders were used to estimate associations
296 hts the need to consider sampling devices as potential confounders when comparing multiple studies an
299 hat it was unlikely that adjusting for these potential confounders would have radically changed the f
300 nservative reanalyses under consideration of potential confounders yielded nominally lower but compar
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