1 ciated with melanoma after tanning inability
was adjusted for.
2 istically significant when phosphorus intake
was adjusted for.
3 ignificant when weight gain during follow-up
was adjusted for.
4 d) while patient- and hospital-level factors
were adjusted for.
5 bles, temperature or steam generation cannot
be adjusted for a given heat input.
6 Logistic regression models
were adjusted for a broad range of potential confounding
7 heric inversions and biosphere models, which
were adjusted for a consistent flux definition, showed a
8 Analyses
were adjusted for a minimal sufficient confounding struc
9 All models
were adjusted for a minimum of 18 a priori determined po
10 Log-linear regressions
were adjusted for a priori selected covariates to determ
11 Models
were adjusted for a stroke or CHD risk score and behavio
12 GF levels predicted decreased survival after
being adjusted for age, PAH subtype, invasive hemodynami
13 hazard to vary according to neighborhood and
was adjusted for age, race and ethnic group, and ownersh
14 Relative risk of death
was adjusted for age, sex, race/ethnicity, and season us
15 ividual hazard-based transmission model that
was adjusted for age, vaccination, and household size.
16 Prevalences
were adjusted for age and CD4 cell count.
17 Models
were adjusted for age and conditioned on calendar day an
18 nsurance status were assessed in models that
were adjusted for age and each of the other factors.
19 Models
were adjusted for age and ethnicity (Ashkenazi Jewish vs
20 mparisons between treated and untreated eyes
were adjusted for age and other confounding variables.
21 All models
were adjusted for age and sex.
22 tistical analyses were performed, all models
were adjusted for age and smoking, and p-values were adj
23 between CFH and ARMS2 polymorphisms and RMDA
were adjusted for age and smoking.
24 Analyses
were adjusted for age at brachytherapy, year of treatmen
25 Common variants (MAF > 0.05)
were adjusted for age at cancer diagnosis, CED, and top
26 developed to identify predictors, and models
were adjusted for age at diagnosis, sex, and parent educ
27 All models
were adjusted for age at time of scan, gender, ethnicity
28 Cox regression models
were adjusted for age, AF risk factors, inflammatory, an
29 Generalized linear models
were adjusted for age, age squared, sex, height, princip
30 apping and family-based association analyses
were adjusted for age, age(2), sex, body mass index, and
31 Our HRs
were adjusted for age, baseline educational level, marit
32 All results
were adjusted for age, body mass index, and mean arteria
33 Measures of association
were adjusted for age, diabetes, smoking, American Socie
34 Models
were adjusted for age, education, dental visit frequency
35 Multivariate Cox regression models
were adjusted for age, education, smoking, physical acti
36 Multivariate Cox regression models
were adjusted for age, family history of hypertension, b
37 Outcomes
were adjusted for age, gender, admission type, severity
38 Analyses
were adjusted for age, gender, education and social clas
39 Multivariable models
were adjusted for age, gender, race, diagnosis, central
40 Results
were adjusted for age, height, and weight.
41 Models
were adjusted for age, income, smoking status, frequency
42 Calculations
were adjusted for age, National Institutes of Health Str
43 The HRs
were adjusted for age, pathological T category, tumor di
44 Models
were adjusted for age, principal diagnosis, and comorbid
45 Models
were adjusted for age, race or ethnicity, smoking, hepat
46 Analyses
were adjusted for age, race, breast density, baseline ex
47 Cox proportional hazards models
were adjusted for age, race, education, body mass index,
48 Results
were adjusted for age, race/ethnicity, sex, height, weig
49 Survival models
were adjusted for age, sex, alcohol intake, smoking hist
50 MSS) estimates up to 5 years after diagnosis
were adjusted for age, sex, and 8th edition American Joi
51 Models
were adjusted for age, sex, and BMO area.
52 All analyses
were adjusted for age, sex, and education.
53 Association analyses
were adjusted for age, sex, and principal components in
54 ccounted for the complex sampling design and
were adjusted for age, sex, and race.
55 Analyses
were adjusted for age, sex, and wealth.
56 Multiple variable analyses
were adjusted for age, sex, baseline severity and time-t
57 Cox models for these outcomes
were adjusted for age, sex, body mass index, hypertensio
58 use of Cox proportional hazards models that
were adjusted for age, sex, body mass index, smoking sta
59 The regression analyses
were adjusted for age, sex, calendar time, and origin.
60 P values
were adjusted for age, sex, carotid artery site, and fam
61 All risk estimates
were adjusted for age, sex, comorbidity, type of antiref
62 Associations
were adjusted for age, sex, education, diabetes status,
63 HRs
were adjusted for age, sex, educational level, marital s
64 All models
were adjusted for age, sex, ethnicity, and waist circumf
65 Analyses
were adjusted for age, sex, ethnicity, socioeconomic sta
66 Models
were adjusted for age, sex, height, weight, pack-years,
67 e, whereas in the pharmacogenomic study, HRs
were adjusted for age, sex, history of myocardial infarc
68 Multivariable models of stress and BDR
were adjusted for age, sex, income, environmental tobacc
69 Data
were adjusted for age, sex, lower rate limit, percent at
70 These results
were adjusted for age, sex, mean arterial pressure, and
71 Models
were adjusted for age, sex, parasitemia, inflammation, a
72 Data
were adjusted for age, sex, race/ethnicity, baseline bod
73 Models
were adjusted for age, sex, race/ethnicity, education, e
74 Models
were adjusted for age, sex, race/ethnicity, education, s
75 Partial correlation coefficients
were adjusted for age, sex, race/ethnicity, height, weig
76 Analyses
were adjusted for age, sex, race/ethnicity, region of re
77 Statistical analyses
were adjusted for age, sex, randomized treatment, region
78 Regression models
were adjusted for age, sex, season, and pubertal stage.
79 odels accounted for familial relatedness and
were adjusted for age, sex, total arsenic levels, and po
80 d using Cox proportional hazards models that
were adjusted for age, sex, total energy intake, smoking
81 In multivariable models that
were adjusted for age, sex, urban or rural residence, an
82 Linear models
were adjusted for age, sex, years of education, and apol
83 HRs
were adjusted for age, smoking status, and education lev
84 Joint effect ORs
were adjusted for age, smoking, iris pigmentation, self-
85 ORs
were adjusted for age, study site, language, income, las
86 Analyses
were adjusted for age, Tyrer-Cuzick risk, smoking, use o
87 Models
were adjusted for age, years enrolled, parity, and race/
88 ar depth, changes in neuroretinal parameters
were adjusted for age-related reduction.
89 Final multivariate models
were adjusted for age.
90 These data
were adjusted for all-cause mortality with data from the
91 The baseline model
was adjusted for alternative age groups and high-risk dy
92 raction of genes expressed in a cell, should
be adjusted for as a source of nuisance variation.
93 Seroprevalence 95% confidence intervals (CI)
were adjusted for assay sensitivity and specificity.
94 elated to subsequent mortality, and analysis
was adjusted for baseline ePAD.
95 or completely recovered] to 6 [death]) that
was adjusted for baseline stroke severity.
96 Estimates
were adjusted for baseline age, sex, proteinuria and GFR
97 The analyses, which
were adjusted for baseline age, sex, race, history of hy
98 Models
were adjusted for baseline characteristics and severity
99 , or colorectal cancer, and RRs with 95% CIs
were adjusted for baseline characteristics associated wi
100 Regression models
were adjusted for baseline function and patient and tumo
101 s with time-varying number of RFs in control
were adjusted for baseline number of RFs in control, cli
102 -mediated disease, omalizumab dosages should
be adjusted for body weight alone, independently of tota
103 d by age at recruitment and study center and
were adjusted for breast cancer risk factors.
104 Remaining baseline differences
were adjusted for by multivariate analysis.
105 edure, odds ratios looking at country effect
were adjusted for cadre effect for these two countries.
106 Models
were adjusted for calendar time and other potential conf
107 All analyses
were adjusted for cardiometabolic risk factors.
108 Cox proportional hazards regression models
were adjusted for cardiovascular disease risk factors.
109 In models that
were adjusted for cardiovascular risk factors, severity
110 Multivariable models
were adjusted for child age, sex, race/ethnicity, and ne
111 In a logistic regression analysis that
was adjusted for cholesterol and the other tocopherol, l
112 Results
were adjusted for clinical readmission risk.
113 Prevalence ratios (PR)
were adjusted for cluster effects and baseline character
114 Analyses were by intention to treat, and
are adjusted for clustering within schools and for basel
115 d participants contributing outcome data and
were adjusted for clustering at the clinic level.
116 Linear mixed models that
were adjusted for clustering of providers assessed betwe
117 Multivariable models
were adjusted for comorbidity status (incidental vs cont
118 were used to estimate odds ratios (ORs) that
were adjusted for comorbidity, education level, and inco
119 These data
were adjusted for completeness using indirect demographi
120 females, and parous females; these estimates
were adjusted for confounders and accommodated concentra
121 The models
were adjusted for confounders such as body size.
122 Analyses
were adjusted for confounders such as discharge National
123 Models
were adjusted for confounders, including other Healthy E
124 seline CH and follow-up ALCSD rate of change
was adjusted for confounding factors, including age, int
125 Analyses
were adjusted for confounding by time, cluster effects,
126 Analyses
were adjusted for confounding using inverse probability
127 Models
were adjusted for country fixed effects, survey-year fix
128 Multivariate models
were adjusted for covariates (age, sex, tumor grade, T/N
129 Rate ratios
were adjusted for covariates (diabetes mellitus, myocard
130 Analyses
were adjusted for covariates and multiple hypothesis tes
131 Estimates
were adjusted for delay in diagnosis and reporting by we
132 Cox proportional hazards models
were adjusted for demographic and cardiovascular risk fa
133 (aRRs) and absolute risk differences (ARDs)
were adjusted for demographic characteristics and comorb
134 repeated measure logistic regression models
were adjusted for demographic characteristics, clinical
135 Comparison estimates
were adjusted for demographic differences.
136 These analyses
were adjusted for demographic factors known to influence
137 Logistic and linear regression models
were adjusted for demographic, lifestyle, and dietary va
138 All analyses
were adjusted for demographics and standard COPD risk fa
139 Linear regression models
were adjusted for demographics, anthropometrics, smoking
140 Models
were adjusted for demographics, behaviors, and physiolog
141 persons with HCV infection to those without,
were adjusted for demographics, BMI, C-reactive protein,
142 DeltaEF (multiple linear regression models)
were adjusted for demographics, traditional cardiovascul
143 Models
were adjusted for demographics, viral loads, CD4 counts,
144 that the spectral matching settings need to
be adjusted for each project.
145 Logistic regression models
were adjusted for education, other early life characteri
146 were studied cross-sectionally, and analyses
were adjusted for effects of confounding variables.
147 predicted all-cause mortality in models that
were adjusted for established risk predictors, but assoc
148 Models
were adjusted for estimated cell type proportions, age,
149 Hazard ratios
were adjusted for estimated glomerular filtration rate a
150 When methylation values
were adjusted for estimated leukocyte fractions, 541 pro
151 nd cancer detection rates relative to DM and
was adjusted for examination-level characteristics.
152 e adjusted for age and smoking, and p-values
were adjusted for false discovery.
153 When comorbidities
were adjusted for,
FFMI in quartile 4 (>19.5 kg/m(2)) st
154 Two-pollutant models
were adjusted for fine particles with aerodynamic diamet
155 Data
were adjusted for gestational age and use of probiotics.
156 All analyses
were adjusted for gestational age, sex, birth weight, ma
157 Survival analyses
were adjusted for guarantee-time bias controlling for kn
158 Analyses
were adjusted for head motion, age and sex, and controll
159 Models
were adjusted for health and lifestyle factors, dietary
160 The statistical analysis
was adjusted for hospital and for risk factors.
161 The Receiver Operating Characteristic
was adjusted for ICU and hospital length of stay along w
162 Cox regression was used, and models
were adjusted for important baseline and clinical covari
163 HRs
were adjusted for important confounders and immortal tim
164 cts in a study or batch effects that need to
be adjusted for in order to better isolate the effects o
165 ery and nonsurgical survey), diabetes status
was adjusted for in a multivariate Poisson regression mo
166 with central corneal thickness (CCT), which
was adjusted for in all analyses.
167 g background risk of Kawasaki disease by age
was adjusted for in both designs.
168 in background and procedural characteristics
were adjusted for in a multivariate Cox regression model
169 Analyses
were adjusted for income, parental education, maternal s
170 Estimates of association
were adjusted for individual and contextual sociodemogra
171 Regression models
were adjusted for individual sociodemographic and clinic
172 Models
were adjusted for individual, maternal, and household co
173 All models
were adjusted for individual-level predictors including
174 SR
was adjusted for inflammation in the Zambian children.
175 Costs
were adjusted for inflation and reported in 2015 dollars
176 All nominal dollars
were adjusted for inflation by converting to 2014 US dol
177 Costs
were adjusted for inflation to 2014 US dollars.
178 All costs and benefits
were adjusted for inflation to 2019 United States dollar
179 Prices
were adjusted for inflation.
180 Costs are reported in 2012 US dollars and
were adjusted for inflation.
181 Models
were adjusted for inverse probability of sampling weight
182 or socioeconomic differences that could not
be adjusted for is unknown.
183 sociations with total soft-drink consumption
were adjusted for juice and nectar consumption and vice
184 id precursor protein after the latter values
were adjusted for kinetic isotope effects.
185 Associations between FeNO and HIV status
were adjusted for known potential confounders.
186 were adjusted for known survival predictors, including p
187 eneralized estimating equation models, which
were adjusted for lifestyle, biological, and other dieta
188 Risk ratios
were adjusted for male partner testing history and recru
189 Although our models
are adjusted for many potential confounders, there are a
190 Hazard ratios (HRs)
were adjusted for marital status, immigration status, in
191 All models
were adjusted for maternal age, education, annual househ
192 Relative risks
were adjusted for maternal age, parity, income quintile,
193 Logistic regression models
were adjusted for maternal age, race, education, body ma
194 Analyses
were adjusted for maternal age, race/ethnicity, educatio
195 Analyses
were adjusted for maternal and childhood sociodemographi
196 Estimates
were adjusted for maternal and pregnancy characteristics
197 erved in subgroup analyses (n = 27,395) that
were adjusted for maternal stature (P < 0.001).
198 gthened when sectoral variation of ACA width
was adjusted for mean ACA width.
199 aseline LDL-C measurements, and all analyses
were adjusted for mean LDL-C levels and cardiovascular r
200 All statistical analyses
were adjusted for mean putamen binding.
201 rence was no longer evident after the models
were adjusted for mistreatment (odds ratio, 0.90; 95% CI
202 as a sequential decision-making process that
is adjusted for motor noise, and raises interesting ques
203 The threshold for statistical significance
was adjusted for multiple comparisons using Bonferroni c
204 P-values
were adjusted for multiple comparisons, and permutation
205 iations became non-significant when analyses
were adjusted for multiple comparisons.
206 erformed using Phyloseq and DESeq2; P-values
were adjusted for multiple comparisons.
207 ry was analyzed separately, and the P values
were adjusted for multiple comparisons.
208 d across tertiles; P values for significance
were adjusted for multiple comparisons.
209 ed model for repeated measures, and p values
were adjusted for multiplicity.
210 When treatment-requiring ROP
was adjusted for,
no significant association between GA
211 ence remained in multivariable analysis that
was adjusted for nodal status, prior use of hormone repl
212 cell's fate and developmental stage and that
is adjusted for optimal cell function.
213 truncating variants, but our method can also
be adjusted for other types of ASE effects.
214 However, after analysis
was adjusted for other cancer therapies and other covari
215 Outcomes
were adjusted for other prognostic variables including N
216 We also conducted analyses that
were adjusted for other substance use disorder criteria
217 All models
were adjusted for patient and hospital characteristics t
218 Multivariable linear probability models
were adjusted for patient and hospital characteristics.
219 Odds ratios (ORs) and 95% CIs
were adjusted for patient demographics and baseline risk
220 All models
were adjusted for patient demographics, comorbidities, s
221 Hospital-level SLNB positivity rates
were adjusted for patient- and tumor factors.
222 Sensitivity analyses
were adjusted for patients who crossed over from placebo
223 Rates
were adjusted for population differences in age, sex, ra
224 Linear mixed models
were adjusted for postpartum age and infant sex.
225 use of conditional logistic regression that
was adjusted for potential confounders.
226 Every analysis
was adjusted for potential confounders.
227 multivariate linear regression analysis that
was adjusted for potential confounding factors including
228 The model
was adjusted for potential confounding factors, includin
229 Models
were adjusted for potential confounders and energy misre
230 All models
were adjusted for potential confounders, including demog
231 Analyses
were adjusted for potential confounders.
232 a range of other known ADPKD manifestations
were adjusted for potential confounders.
233 ase HIV risk, we only used RR estimates that
were adjusted for potential confounders.
234 Analyses
were adjusted for potential confounding due to age, sex,
235 Rate ratios
were adjusted for potential confounding variables.
236 Logistic regression models
were adjusted for potential demographic confounders and
237 Outcomes
were adjusted for potential sociodemographic, maternity,
238 When the FCAT test scores
were adjusted for potentially confounding maternal and i
239 Hazard ratios (HRs)
were adjusted for predictors of multiple-type infection.
240 Analyses
were adjusted for prespecified covariates.
241 ion models of 30-day postoperative mortality
were adjusted for procedure year, age, Charlson Comorbid
242 Outcomes
were adjusted for prognostic variables and analyzed usin
243 Statistical models
were adjusted for race, sex, smoking, body mass index, a
244 Multivariable logistic regression
was adjusted for relevant demographic characteristics.
245 Models
were adjusted for relevant child- and county-level chara
246 Models
were adjusted for relevant confounders.
247 infusion every 8 h) for 7-14 days; regimens
were adjusted for renal function.
248 All analyses
were adjusted for repeated measures per patient.
249 After potential confounders
were adjusted for,
risk of overweight was 15% lower in p
250 Separate models
were adjusted for screen-detected and interval cancers a
251 e age pattern of the hazard ratios that have
been adjusted for selection.
252 All hazard ratios (HRs) and 95% CIs
were adjusted for several potential confounders using Co
253 ressure (BP) is measured in percentiles that
are adjusted for sex, age, and height percentile in chil
254 This
was adjusted for sex, geographical region, and birth per
255 alyzed with survival analysis techniques and
were adjusted for sex, age, calendar period, cohabitatio
256 Models
were adjusted for sex, age, education, and income (total
257 Models
were adjusted for sex, age, education, baseline test sco
258 ratios (IRRs) and absolute risk differences
were adjusted for sex, age, smoking status, obesity, soc
259 es between participants with and without HIV
were adjusted for sex, education, age, country of birth,
260 Analyses
were adjusted for sex, parental postnatal smoking, psych
261 s between lipid levels and clinical outcomes
were adjusted for sex, passive smoking, and body mass in
262 Analyses
were adjusted for sex, study center, and educational lev
263 lues were inverse normalized, and all traits
were adjusted for significant covariate effects of age a
264 Relative risk (RR) estimates
were adjusted for site, receipt of another vaccine durin
265 Models
were adjusted for socio-economic development and wider h
266 s from multivariate linear regression models
were adjusted for sociodemographic characteristics and f
267 ts of physical activity on mortality and CVD
were adjusted for sociodemographic factors and other ris
268 s of incident chronic disease and death, and
were adjusted for sociodemographic, behavioral, and clin
269 All models
were adjusted for sociodemographic, criminographic, and
270 Models
were adjusted for sociodemographics, cardiovascular dise
271 Models
were adjusted for socioeconomic, health, and demographic
272 egression models assessed yearly changes and
were adjusted for study center, race/ethnicity, gestatio
273 Logistic regression analyses
were adjusted for surgical factors and patients' preoper
274 model in which the impact of each covariate
was adjusted for that of all others.
275 f tests, both phenotypic and genotypic, must
be adjusted for the correlations between them.
276 In a secondary analysis that
was adjusted for the number of patients per resident phy
277 Results
were adjusted for the effects of other common infections
278 aggregation of breast and ovarian cancer and
were adjusted for the family-specific ascertainment sche
279 Analyses
were adjusted for the following potential confounders: a
280 Analyses
were adjusted for the hospital and characteristics of th
281 Estimates
were adjusted for the presence of comorbidities and are
282 Analyses
were adjusted for the prognostic stage, size, grade, and
283 on analyses were performed, and all analyses
were adjusted for the survey design.
284 In time-dependent models that
were adjusted for the use of a lipid-lowering medication
285 All models
were adjusted for total energy intake, age, body mass in
286 ain magnetic resonance imaging outcomes also
were adjusted for total intracranial volume.
287 Neuroimaging metrics
were adjusted for total intracranial volume.
288 Models
were adjusted for traditional CVD risk factors.
289 in resistance) with subclinical CVD measures
were adjusted for traditional CVD risk factors.
290 Models
were adjusted for traditional risk factors, low-density
291 Estimated hazard ratios (HRs)
were adjusted for transmission risk group, sex, age, yea
292 Analyses
were adjusted for underlying mortality risk (age, Injury
293 Analyses
were adjusted for underlying time trends, quarter of yea
294 ear all-cause mortality, and survival models
were adjusted for variables that confounded the chloride
295 Analysis
was adjusted for whether women had been age-eligible for
296 cess and quality and diet, but these factors
were adjusted for with use of county-specific random int
297 gestational age, breast-feeding, and gender
were adjusted for within each multi-variable model.
298 Models
were adjusted for within-ICU correlation, patient- and I
299 The model
was adjusted for year of admission, length of stay, type
300 Analyses
were adjusted for year of birth (ie, partially adjusted)