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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 ematosus was excluded from all patients as a potential confounder.
4 isk using Cox regression models adjusted for potential confounders.
5 sing multiple linear regression adjusted for potential confounders.
6 ustment for sex, breastfeeding duration, and potential confounders.
7 in current prophylaxis guidelines and other potential confounders.
8 cessation of the intervention, adjusted for potential confounders.
9 h an increased risk of death, independent of potential confounders.
10 using random-effects models controlling for potential confounders.
11 x proportional hazards models, adjusting for potential confounders.
12 hods and applied limited risk-adjustment for potential confounders.
13 sociations were independent of BMI and other potential confounders.
14 d drinkers among population once adjusted by potential confounders.
15 or confounding from all spatial and temporal potential confounders.
16 associated with FGF23 level, independent of potential confounders.
17 = 0.004) decrease in CRP after adjusting for potential confounders.
18 d infant mortality rates with adjustment for potential confounders.
19 by low-dose aspirin use after adjusting for potential confounders.
20 = .001) after adjustment for a wide range of potential confounders.
21 in surgical mortality studies to control for potential confounders.
22 contraception, breast-cancer diagnoses, and potential confounders.
23 Analyses were adjusted for potential confounders.
24 ox proportional hazards models, adjusted for potential confounders.
25 ity scores based on patients' and hospitals' potential confounders.
26 variable logistic regression controlling for potential confounders.
27 ional) and each CHD phenotype, adjusting for potential confounders.
28 probabilities regression models adjusted for potential confounders.
29 hips persisted after adjustment for multiple potential confounders.
30 FeNO and HIV status were adjusted for known potential confounders.
31 ted using logistic regression to account for potential confounders.
32 dices and ovarian cancer risk, adjusting for potential confounders.
33 parametric survival models that adjusted for potential confounders.
34 re used to estimate odds ratios adjusted for potential confounders.
35 for demographic, socioeconomic, and clinical potential confounders.
36 y logistic regression analysis adjusting for potential confounders.
37 0.73, 1.00) after adjustment for a range of potential confounders.
38 MVO (beta=0.18; P=0.001) after adjusting for potential confounders.
39 n terms of weight, body mass index, and most potential confounders.
40 istic regression was performed to adjust for potential confounders.
41 d medical conditions, medications, and other potential confounders.
42 g MIG or OG, using year of surgery and other potential confounders.
43 y linear or logistic regression adjusted for potential confounders.
44 l [CI], 1.03-1.43; P = 0.02), independent of potential confounders.
45 logistic, and Cox regression to control for potential confounders.
46 ean population even after adjusting by other potential confounders.
47 f the data and generally limited control for potential confounders.
48 al hazards regression was used to adjust for potential confounders.
49 l costs, and financial burden, adjusting for potential confounders.
50 ressions were performed, while adjusting for potential confounders.
51 g logistic regression analyses adjusting for potential confounders.
52 confirmed these findings while adjusting for potential confounders.
53 known ADPKD manifestations were adjusted for potential confounders.
54 ogeneous data with suboptimal adjustment for potential confounders.
55 ients versus controls, while controlling for potential confounders.
56 ufficient vitamin B-6 status, independent of potential confounders.
57 ated with exposures variables, adjusting for potential confounders.
58 ds regression models were used to adjust for potential confounders.
59 trolling for body mass index (BMI) and other potential confounders.
60 simple linear regression models adjusted for potential confounders.
61 We adjusted analyses for recorded potential confounders.
62 I, 0.58-0.88; P = .002), after adjusting for potential confounders.
63 f ATE was conducted to control for available potential confounders.
64 ent for family history of diabetes and other potential confounders.
65 .90-4.67) after multivariable adjustment for potential confounders.
66 ards regression models, while accounting for potential confounders.
67 ltiple linear regression models adjusted for potential confounders.
68 als, adjusting for tobacco smoking and other potential confounders.
69 5% confidence intervals while accounting for potential confounders.
70 nts and AC- participants after adjusting for potential confounders.
71 ognitive deficits are robust or explained by potential confounders.
72 adults without MDD after adjustment for many potential confounders.
73 ultivariable linear regression, adjusted for potential confounders.
74 atistically significant after adjustment for potential confounders.
75 WAZ changes (P = 0.103) after adjustment for potential confounders.
76 io, 1.34 [1.01-1.79]; p < 0.05) adjusted for potential confounders.
77 ic status and renal function into account as potential confounders.
78 AF with subsequent cancer and to adjust for potential confounders.
79 s stratified by matched set and adjusted for potential confounders.
80 iables, adjusting for matching variables and potential confounders.
81 geQTL can also correct the effects of potential confounders.
82 95% CI, 2.39-138.22; P = .005) adjusting for potential confounders.
83 multivariable linear regression adjusted for potential confounders.
84 adiographs (P = 0.005), when controlling for potential confounders.
85 31) disappeared after further adjustment for potential confounders.
86 CI, 2.00-4.17; P < .001) after adjusting for potential confounders.
87 P = .001) quadrants even after adjusting for potential confounders.
88 portional hazard models, with adjustment for potential confounders.
89 1.981; 95% CI = 1.246-3.149), adjusting for potential confounders.
90 for 52 weeks with and without adjustment for potential confounders.
91 R; 2.66; 95% CI: 1.53, 4.61), independent of potential confounders.
92 ignificant after multivariate adjustment for potential confounders.
93 x, and period of interview, and adjusted for potential confounders.
94 opmental adversities were used to adjust for potential confounders.
95 nal hazards regression model, controlled for potential confounders.
96 -aldosterone system inhibitor use, and other potential confounders.
97 rides, high BP, and MetS after adjusting for potential confounders.
98 al logistic regression that was adjusted for potential confounders.
99 tality or HCC recurrence after adjusting for potential confounders.
100 onal logistic regression with adjustment for potential confounders.
101 imary exposure indicators along with several potential confounders.
102 ) serum magnesium levels while adjusting for potential confounders.
103 Cox proportional hazards model adjusted for potential confounders.
104 egression analysis was performed to evaluate potential confounders.
105 posure and microcephaly status, adjusted for potential confounders.
106 del multivariate associations, adjusting for potential confounders.
107 hted Cox regression models and adjusting for potential confounders.
108 included in regression models adjusting for potential confounders.
109 , sex, leukocyte cell composition, and other potential confounders.
110 t VI with all 3 VRQoL outcomes, adjusted for potential confounders.
111 1.37, 95% CI 1.13-1.67) after adjustment for potential confounders.
112 Every analysis was adjusted for potential confounders.
113 ultivariable regression models adjusting for potential confounders.
114 r List B performance, adjusting for multiple potential confounders.
115 and 95% confidence intervals, adjusting for potential confounders.
116 ease recurrence remained after adjusting for potential confounders.
117 ons remained significant after adjusting for potential confounders.
118 clude that treatment-related differences are potential confounders.
119 nd incidence of kidney stones, adjusting for potential confounders.
120 les and serious complications, adjusting for potential confounders.
121 utcome, ASD, before and after adjustment for potential confounders.
122 using linear regression models adjusted for potential confounders.
123 rse (CIN2+) or grade 3 or worse (CIN3+), and potential confounders.
124 nd RSVH risks across the groups adjusted for potential confounders.
125 justed for baseline mid-thigh muscle CSA and potential confounders.
126 with robust error variance and adjusted for potential confounders.
127 sted for a minimum of 18 a priori determined potential confounders.
128 hin the first 3 months after controlling for potential confounders.
129 CI 1.46-11.71]) after adjusting for several potential confounders.
130 unt for repeat pregnancies and adjusting for potential confounders.
131 I) design, which controls for time-invariant potential confounders.
132 nly used RR estimates that were adjusted for potential confounders.
133 valence ratios and differences, adjusted for potential confounders.
134 using linear regression, with adjustment for potential confounders.
135 h incident HF hospitalization, adjusting for potential confounders.
136 ng into account pre-existing comorbidity and potential confounders.
137 under any genetic models after adjusting for potential confounders.
138 tivariable logistic regression adjusting for potential confounders.
139 e as a time-dependent variable and for other potential confounders.
140 to evaluate associations while adjusting for potential confounders.
141 ality after adjustment for smoking and other potential confounders (1 cup per day: hazard ratio [HR],
142 lustrate how DAGs can be used to identify 1) potential confounders, 2) mediators and the consequences
143 ) and in multivariate analyses adjusting for potential confounders (-5.50, -10.51 to -0.48; p=0.0316)
145 , and period of enrollment, and including as potential confounders a family history of any allergy in
147 tratified analyses by sex, after control for potential confounders, a greater GI was linked to a high
151 ic-spline regression, adjusting for a priori potential confounders (age, type of surgery, support sta
152 Analyses were adjusted for the following potential confounders: age, gender, vascular comorbidity
155 T2D was 0.23 (95% CI 0.19-0.29) adjusted for potential confounders and 0.37 (95% CI 0.27-0.50) furthe
159 of the MDS score and AMD, taking account of potential confounders and the multicenter study design.
161 ained elevated after adjustment for relevant potential confounders and was also observed among never-
162 d algorithm used to select a large number of potential confounders) and by comparing exposed children
163 ilevel linear regression models adjusted for potential confounders, and conducted several sensitivity
164 irected acyclic graphs were used to identify potential confounders, and Cox proportional hazard model
165 neralized linear mixed models, adjusting for potential confounders, and explored effect modification.
166 os (RRs) were calculated with adjustment for potential confounders, and population attributable fract
167 idence interval, 1.0 to 8.0), independent of potential confounders, and tended to develop earlier rec
168 and increased CHD risk was not explained by potential confounders, and there was no evidence of reve
169 tion-to-treat (ITT) population, adjusted for potential confounders at patient level (sex, age) and pr
170 on remained significant after adjustment for potential confounders (B=-0.098; 95% confidence interval
171 conducted stratified analyses to analyze the potential confounders behind these discordant outcomes.
172 nd we determined the impact of adjusting for potential confounders collected from a subset of the coh
175 Only a select set of quality indicators and potential confounders could be ascertained from availabl
176 cular outcomes using Cox models adjusted for potential confounders (demographics, clinical characteri
177 Meta-analysis of studies that adjusted for potential confounders demonstrated that preeclampsia was
179 led adjustment of the observational data for potential confounders did not reduce the divergence from
180 pensity score (PS) was calculated to address potential confounders due to unbalanced distribution of
182 ults remained significant when adjusting for potential confounders (e.g., neuropsychological measures
183 justing for age, sex, and race and for other potential confounders (education, income, region of resi
184 In multivariable models, after adjusting for potential confounders, every doubling of GDF-15 level as
185 PFAS levels by occupation and adjusting for potential confounders, firefighters had higher geometric
186 owing for this association and adjusting for potential confounders, happiness and related measures of
188 ly significant after adjustment for measured potential confounders (HR, 1.19; 95% CI, 1.13-1.24).
189 sociation that remained after adjustment for potential confounders (HR: 69.5; 95% CI: 7.0, 694.6).
192 ls, stratified on donor sex and adjusted for potential confounders, included a recipient sex by curre
193 .21-0.68; p < 0.01]) in models adjusting for potential confounders including age, initial rhythm, tim
194 iation between PPCS and HRQoL, adjusting for potential confounders including age, sex, prior concussi
195 work and at study enrollment, adjusting for potential confounders including airborne total hydrocarb
196 ic and linear regression models adjusted for potential confounders including early respiratory/allerg
197 or of facial recognition after adjusting for potential confounders including glaucoma severity, CS, a
199 (-10) , which persisted after adjustment for potential confounders including pathogenic airway bacter
200 ntibiotics, and failure to control for large potential confounders including patients' presenting sig
201 ever, most studies were unable to adjust for potential confounders including pre-existing comorbiditi
202 ear 6, adjusted for muscle CSA at year 1 and potential confounders including prevalent health conditi
204 ) per status epilepticus day, independent of potential confounders (including fatal etiology, duratio
205 eye VA and the outcome (VRQoL), adjusted for potential confounders, including age, gender, socioecono
206 d) and multivariate models that adjusted for potential confounders, including age, sex, race, baselin
208 tatistically significant after adjusting for potential confounders, including calcium and fiber intak
209 Cox proportional hazard models, adjusted for potential confounders, including cardiovascular risk fac
210 =0.41), and remained so after adjustment for potential confounders, including clinical presentation (
214 nsulin-requiring diabetes was independent of potential confounders, including diabetes duration, and
220 mber of patients per resident physician as a potential confounder, intervention schedules were no lon
222 preeclampsia and ESKD adjusting for several potential confounders: maternal age, body mass index (BM
223 iable Poisson regression model adjusting for potential confounders, mean arterial blood pressure grea
224 sing multivariable regression to control for potential confounders.Measurements and Main Results: Of
225 ic regression models were used to adjust for potential confounders (MI risk factors and HIV-related p
227 ncreased mortality, even after adjusting for potential confounders (odds ratio [95% CI], 1.14 (1.08-1
229 timate HRs with 95% CIs, with adjustment for potential confounders.Of the 4400 participants, 2551 (57
232 a logistic regression model controlling for potential confounders, ozone exposure was associated wit
235 t] using binomial regressions, adjusting for potential confounders, random effects for village, and a
236 study, and although we accounted for several potential confounders, residual confounding cannot be ru
237 decreased risk for AMR after adjustment for potential confounders (risk ratio 0.94 per TTV log level
238 ation, lifestyle factors, and morbidities as potential confounders, rLTL was associated with ALM (bet
241 Conditional logistic regression adjusted on potential confounders (smoking, growing in countryside,
244 orted fresh fruit consumption, adjusting for potential confounders such as age, sex, region, socio-ec
245 f 4 and 6 mo, and research that accounts for potential confounders such as feeding practices and base
247 iabetes as an effect modifier, adjusting for potential confounders such as smoking status, sex, age,
248 ed a propensity score analysis to adjust for potential confounders, such as poorly controlled hyperte
249 s remained significant after controlling for potential confounders, such as substance use disorders.
250 vival analyses were performed, adjusting for potential confounders (tacrolimus trough, variability of
252 18 months; we examined, after adjustment for potential confounders, the associations between breathle
254 the African studies, the paucity of data on potential confounders, the limited statistical power to
257 one loss and with a careful consideration of potential confounders, the risk of a first MI was signif
260 Although our models are adjusted for many potential confounders, there are also unmeasured confoun
261 propensity score matching and adjusting for potential confounders, there was no longer a significant
263 oss-sectional in design and few adjusted for potential confounders, this analysis provides comprehens
265 issues of reverse causality, and adjusts for potential confounders to address gaps and limitations in
266 le logistic regressions, with adjustment for potential confounders, to estimate the associations of n
270 lendar year of patient enrollment, and other potential confounders, vitamin E treatment decreased the
273 ion for IVF, other fertility treatments, and potential confounders was collected from medical records
275 ession, both without and with adjustment for potential confounders, was used to measure the associati
277 ized estimating equation models adjusted for potential confounders, we evaluated the association betw
282 thropometry and atopy at age of 8 years, and potential confounders were available for 1608 participan
283 hout the entire follow-up period, even after potential confounders were controlled for (P < 0.05).
284 in 2002, major chronic conditions, and other potential confounders were controlled for, men with prob
286 ls and without pleiotropic associations with potential confounders were estimated to explain about 0.
287 ipid measurements, genetics, medication, and potential confounders were extracted from the E3 databas
292 ltiple linear regression models adjusted for potential confounders were used to estimate associations
294 riable Cox regression analyses, adjusted for potential confounders, were used to prospectively study
295 hts the need to consider sampling devices as potential confounders when comparing multiple studies an
296 rtional hazards model adjusting for multiple potential confounders, when compared to a baseline 25(OH
297 ted with total mortality after adjusting for potential confounders, whereas MUFA-As were associated w
300 hat it was unlikely that adjusting for these potential confounders would have radically changed the f