1 istically significant when phosphorus intake
was adjusted for.
2 d) while patient- and hospital-level factors
were adjusted for.
3 bles, temperature or steam generation cannot
be adjusted for a given heat input.
4 Logistic regression models
were adjusted for a broad range of potential confounding
5 Log-linear regressions
were adjusted for a priori selected covariates to determ
6 l-cause mortality HRs for the AHS-2 subjects
were adjusted for a range of lifestyle and sociodemograp
7 Analyses
were adjusted for a range of patient and tumor character
8 Models
were adjusted for a stroke or CHD risk score and behavio
9 st relative to those who ate breakfast daily
were adjusted for adiposity, the differences were no lon
10 Estimates
are adjusted for age group and area of residence.
11 individualised records allowed all models to
be adjusted for age, sex, deprivation, risk status relat
12 GF levels predicted decreased survival after
being adjusted for age, PAH subtype, invasive hemodynami
13 Relative risk of death
was adjusted for age, sex, race/ethnicity, and season us
14 nsurance status were assessed in models that
were adjusted for age and each of the other factors.
15 Models
were adjusted for age and ethnicity (Ashkenazi Jewish vs
16 mparisons between treated and untreated eyes
were adjusted for age and other confounding variables.
17 Comparisons between groups
were adjusted for age and pupil size.
18 All models
were adjusted for age and sex.
19 tistical analyses were performed, all models
were adjusted for age and smoking, and p-values were adj
20 Analyses
were adjusted for age at brachytherapy, year of treatmen
21 developed to identify predictors, and models
were adjusted for age at diagnosis, sex, and parent educ
22 The analyses
were adjusted for age at outcome assessment, prepregnanc
23 All models
were adjusted for age at time of scan, gender, ethnicity
24 Cox regression models
were adjusted for age, AF risk factors, inflammatory, an
25 apping and family-based association analyses
were adjusted for age, age(2), sex, body mass index, and
26 All results
were adjusted for age, body mass index, and mean arteria
27 Measures of association
were adjusted for age, diabetes, smoking, American Socie
28 Models
were adjusted for age, education, dental visit frequency
29 Models
were adjusted for age, education, disease duration, lang
30 Multivariate Cox regression models
were adjusted for age, education, smoking, physical acti
31 as compared with those who had never smoked,
were adjusted for age, educational level, adiposity, and
32 Multivariate Cox regression models
were adjusted for age, family history of hypertension, b
33 All relationships
were adjusted for age, gender and socioeconomic status (
34 Outcomes
were adjusted for age, gender, admission type, severity
35 Analyses
were adjusted for age, gender, race, low income, immunos
36 Models
were adjusted for age, income, smoking status, frequency
37 Calculations
were adjusted for age, National Institutes of Health Str
38 The HRs
were adjusted for age, pathological T category, tumor di
39 Models
were adjusted for age, principal diagnosis, and comorbid
40 Models
were adjusted for age, race or ethnicity, smoking, hepat
41 Cox proportional hazards models
were adjusted for age, race, education, body mass index,
42 Results
were adjusted for age, race/ethnicity, sex, height, weig
43 Survival models
were adjusted for age, sex, alcohol intake, smoking hist
44 MSS) estimates up to 5 years after diagnosis
were adjusted for age, sex, and 8th edition American Joi
45 Models
were adjusted for age, sex, and BMO area.
46 Rate ratios
were adjusted for age, sex, and co-morbidity using multi
47 ccounted for the complex sampling design and
were adjusted for age, sex, and race.
48 All analyses
were adjusted for age, sex, and serum total IgE level.
49 Multiple variable analyses
were adjusted for age, sex, baseline severity and time-t
50 When the models
were adjusted for age, sex, BMI, ethnicity, and metaboli
51 use of Cox proportional hazards models that
were adjusted for age, sex, body mass index, smoking sta
52 The regression analyses
were adjusted for age, sex, calendar time, and origin.
53 P values
were adjusted for age, sex, carotid artery site, and fam
54 All models
were adjusted for age, sex, ethnicity, and waist circumf
55 All models
were adjusted for age, sex, ethnicity, hypertension, and
56 Results
were adjusted for age, sex, field center, weekend, seque
57 V1 and length of hospital stay and mortality
were adjusted for age, sex, height, body mass index, soc
58 Models
were adjusted for age, sex, height, weight, pack-years,
59 All multivariate models
were adjusted for age, sex, household income, and princi
60 All multivariate models
were adjusted for age, sex, household income, atopy (>/=
61 Comparisons
were adjusted for age, sex, hypertension, diabetes, and
62 Multivariable models of stress and BDR
were adjusted for age, sex, income, environmental tobacc
63 Data
were adjusted for age, sex, lower rate limit, percent at
64 statistical parametric mapping software and
were adjusted for age, sex, manual laterality, and Natio
65 These results
were adjusted for age, sex, mean arterial pressure, and
66 Results
were adjusted for age, sex, race, body mass index, ankle
67 Data
were adjusted for age, sex, race/ethnicity, baseline bod
68 Models
were adjusted for age, sex, race/ethnicity, education, e
69 Analyses
were adjusted for age, sex, race/ethnicity, region of re
70 Statistical analyses
were adjusted for age, sex, randomized treatment, region
71 Regression models
were adjusted for age, sex, season, and pubertal stage.
72 The analyses
were adjusted for age, sex, social class, and employment
73 calculated using Cox regression models that
were adjusted for age, sex, tobacco smoking, alcohol dri
74 odels accounted for familial relatedness and
were adjusted for age, sex, total arsenic levels, and po
75 Models
were adjusted for age, sex, treatment, and BMI.
76 In multivariable models that
were adjusted for age, sex, urban or rural residence, an
77 Linear models
were adjusted for age, sex, years of education, and apol
78 HRs
were adjusted for age, smoking status, and education lev
79 Joint effect ORs
were adjusted for age, smoking, iris pigmentation, self-
80 ORs
were adjusted for age, study site, language, income, las
81 Analyses
were adjusted for age, Tyrer-Cuzick risk, smoking, use o
82 Models
were adjusted for age, years enrolled, parity, and race/
83 ar depth, changes in neuroretinal parameters
were adjusted for age-related reduction.
84 Final multivariate models
were adjusted for age.
85 Analyses
were adjusted for age; sex; race/ethnicity; US region of
86 These data
were adjusted for all-cause mortality with data from the
87 The baseline model
was adjusted for alternative age groups and high-risk dy
88 therapeutic agents to the brain may need to
be adjusted for application in Alzheimer's disease.
89 raction of genes expressed in a cell, should
be adjusted for as a source of nuisance variation.
90 When the analysis
was adjusted for attendance, we did not find a significa
91 elated to subsequent mortality, and analysis
was adjusted for baseline ePAD.
92 Estimates
were adjusted for baseline age, sex, proteinuria and GFR
93 The analyses, which
were adjusted for baseline age, sex, race, history of hy
94 Models
were adjusted for baseline body mass index (BMI), race/e
95 Hazard ratios
were adjusted for baseline characteristics.
96 s with time-varying number of RFs in control
were adjusted for baseline number of RFs in control, cli
97 Means
were adjusted for baseline PPD, education, and cigarette
98 Between-group analyses regarding QOL
were adjusted for baseline values and gender.
99 Study results
were adjusted for biases and combined, first in a random
100 Linear models
were adjusted for BMI, occupational social class and dia
101 d by age at recruitment and study center and
were adjusted for breast cancer risk factors.
102 sociations persisted when pre-morbid ability
was adjusted for,
but as expected were no longer statist
103 Remaining baseline differences
were adjusted for by multivariate analysis.
104 edure, odds ratios looking at country effect
were adjusted for cadre effect for these two countries.
105 Models
were adjusted for calendar time and other potential conf
106 All analyses
were adjusted for cardiometabolic risk factors.
107 Cox proportional hazards regression models
were adjusted for cardiovascular disease risk factors.
108 In models that
were adjusted for cardiovascular risk factors, severity
109 he 1-year mortality after hospital discharge
was adjusted for case-mix differences by a set of determ
110 Comparisons
were adjusted for CD4(+) lymphocyte cell count.
111 Primary analyses
were adjusted for CD4(+) T-cell count, smoking, and hepa
112 Multivariable models
were adjusted for child age, sex, race/ethnicity, and ne
113 e assessed whether treatment intensity could
be adjusted for children and young adults according to M
114 In a logistic regression analysis that
was adjusted for cholesterol and the other tocopherol, l
115 Scanning protocols
were adjusted for clinical indication and patient weight
116 Prevalence ratios (PR)
were adjusted for cluster effects and baseline character
117 Analyses were by intention to treat, and
are adjusted for clustering within schools and for basel
118 Linear mixed models that
were adjusted for clustering of providers assessed betwe
119 Multivariable models
were adjusted for comorbidity status (incidental vs cont
120 were used to estimate odds ratios (ORs) that
were adjusted for comorbidity, education level, and inco
121 These data
were adjusted for completeness using indirect demographi
122 The intention-to-treat analysis that
was adjusted for confounders showed no significant effec
123 females, and parous females; these estimates
were adjusted for confounders and accommodated concentra
124 The models
were adjusted for confounders such as body size.
125 ic regression and simple regression analyses
were adjusted for confounding variables.
126 Analyses
were adjusted for continental ancestries, socioeconomic
127 Analyses
were adjusted for conventional AF risk factors, use of a
128 Models
were adjusted for country fixed effects, survey-year fix
129 Multivariate models
were adjusted for covariates (age, sex, tumor grade, T/N
130 Rate ratios
were adjusted for covariates (diabetes mellitus, myocard
131 Analyses
were adjusted for covariates associated with pneumonia a
132 y time-series analysis was used, and results
were adjusted for day of the week, temperature, barometr
133 Estimates
were adjusted for delay in diagnosis and reporting by we
134 The regression portion of the model
was adjusted for demographic and disease characteristics
135 Cox proportional hazards models
were adjusted for demographic and cardiovascular risk fa
136 repeated measure logistic regression models
were adjusted for demographic characteristics, clinical
137 ed in February 2010 and comparison estimates
were adjusted for demographic differences.
138 Comparison estimates
were adjusted for demographic differences.
139 Logistic and linear regression models
were adjusted for demographic, lifestyle, and dietary va
140 Linear regression models
were adjusted for demographics, anthropometrics, smoking
141 Models
were adjusted for demographics, behaviors, and physiolog
142 persons with HCV infection to those without,
were adjusted for demographics, BMI, C-reactive protein,
143 Multivariable hierarchical regression models
were adjusted for demographics, insurance status, and co
144 DeltaEF (multiple linear regression models)
were adjusted for demographics, traditional cardiovascul
145 Sex-specific repeated-measures analyses that
were adjusted for dietary recall order and recall day of
146 n of acquired infection rates between groups
was adjusted for differences at baseline.
147 that the spectral matching settings need to
be adjusted for each project.
148 Overall and disease-free survival (DFS)
were adjusted for effects of significant patient-, disea
149 In these models, MIP
was adjusted for either 1) "village-like" time-independe
150 predicted all-cause mortality in models that
were adjusted for established risk predictors, but assoc
151 When methylation values
were adjusted for estimated leukocyte fractions, 541 pro
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 Multivariate models
were adjusted for gender, gestational age, and mothers s
155 All analyses
were adjusted for gestational age, sex, birth weight, ma
156 Analyses
were adjusted for head motion, age and sex, and controll
157 Center-specific outcomes should
be adjusted for HIV donor and recipient status.
158 The statistical analysis
was adjusted for hospital and for risk factors.
159 In addition, estimates
were adjusted for hospitalization for nausea and vomitin
160 Analyses
were adjusted for imbalances in baseline predictors of o
161 Cox regression was used, and models
were adjusted for important baseline and clinical covari
162 in background and procedural characteristics
were adjusted for in a multivariate Cox regression model
163 These
were adjusted for in subsequent analyses.
164 ociated with ME duration or patient outcomes
were adjusted for in the analyses.
165 Multivariable models
were adjusted for income and stratified by sex.
166 Multivariate models
were adjusted for indicators of socioeconomic status and
167 Estimates of association
were adjusted for individual and contextual sociodemogra
168 Models
were adjusted for individual, maternal, and household co
169 Models
were adjusted for individual-level and census block grou
170 SR
was adjusted for inflammation in the Zambian children.
171 Values either
were adjusted for inflammation [as measured by C-reactiv
172 al all-cause health care expenditures, which
were adjusted for inflation and reported in 2010 US doll
173 Costs
were adjusted for inflation and reported in 2015 dollars
174 All nominal dollars
were adjusted for inflation by converting to 2014 US dol
175 All hospital charges
were adjusted for inflation to 2009 US dollars.
176 Costs
were adjusted for inflation to 2014 US dollars.
177 Prices
were adjusted for inflation.
178 Costs are reported in 2012 US dollars and
were adjusted for inflation.
179 es, all OCT-brain substructure relationships
were adjusted for intracranial volume.
180 Models
were adjusted for inverse probability of sampling weight
181 or socioeconomic differences that could not
be adjusted for is unknown.
182 sociations with total soft-drink consumption
were adjusted for juice and nectar consumption and vice
183 were adjusted for known survival predictors, including p
184 Multivariate models
were adjusted for lifestyle and CHD risk factors as appr
185 eneralized estimating equation models, which
were adjusted for lifestyle, biological, and other dieta
186 dds ratios (OR) and 95% confidence intervals
were adjusted for major hepatobiliary cancer risk factor
187 Hazard ratios (HRs)
were adjusted for marital status, immigration status, in
188 Analyses
were adjusted for matching variables, comorbidity, cardi
189 Relative risks
were adjusted for maternal age, parity, income quintile,
190 Risk estimates
were adjusted for maternal age, parity, smoking, educati
191 Logistic regression models
were adjusted for maternal age, race, education, body ma
192 Logistic regression models
were adjusted for maternal age, race/ethnicity, educatio
193 Analyses
were adjusted for maternal age, race/ethnicity, educatio
194 Analyses
were adjusted for maternal and childhood sociodemographi
195 Estimates
were adjusted for maternal and pregnancy characteristics
196 Models
were adjusted for maternal characteristics and clustered
197 D, 6,641 with a CHD, and 6,123 controls that
were adjusted for maternal characteristics and tested th
198 Analyses
were adjusted for maternal education level, year of birt
199 erved in subgroup analyses (n = 27,395) that
were adjusted for maternal stature (P < 0.001).
200 aseline LDL-C measurements, and all analyses
were adjusted for mean LDL-C levels and cardiovascular r
201 as a sequential decision-making process that
is adjusted for motor noise, and raises interesting ques
202 The threshold for statistical significance
was adjusted for multiple comparisons using Bonferroni c
203 P-values
were adjusted for multiple comparisons, and permutation
204 erformed using Phyloseq and DESeq2; P-values
were adjusted for multiple comparisons.
205 Statistical analyses
were adjusted for multiple risk factors, including insul
206 r regression with repeated measures; results
were adjusted for multiple testing with Bonferroni corre
207 When treatment-requiring ROP
was adjusted for,
no significant association between GA
208 ence remained in multivariable analysis that
was adjusted for nodal status, prior use of hormone repl
209 Regression models
were adjusted for numerous potential confounders, includ
210 truncating variants, but our method can also
be adjusted for other types of ASE effects.
211 However, after analysis
was adjusted for other cancer therapies and other covari
212 Regression models
were adjusted for other risk factors for strabismus, soc
213 We also conducted analyses that
were adjusted for other substance use disorder criteria
214 All models
were adjusted for patient and hospital characteristics t
215 ry was the main outcome and effect estimates
were adjusted for patient characteristics, surgical spec
216 Odds ratios (ORs) and 95% CIs
were adjusted for patient demographics and baseline risk
217 All models
were adjusted for patient demographics, comorbidities, s
218 Hospital-level SLNB positivity rates
were adjusted for patient- and tumor factors.
219 Sensitivity analyses
were adjusted for patients who crossed over from placebo
220 Differences persist even when our data
were adjusted for per capita gross domestic product.
221 Models
were adjusted for personal and facility characteristics.
222 Multilevel analyses
were adjusted for physician, patient, and structural cov
223 Rates
were adjusted for population differences in age, sex, ra
224 - adjusted odds ratio], where the odds ratio
was adjusted for potential confounders.
225 use of conditional logistic regression that
was adjusted for potential confounders.
226 multivariate linear regression analysis that
was adjusted for potential confounding factors including
227 The model
was adjusted for potential confounding factors, includin
228 hip between in-hospital time and perforation
was adjusted for potential confounding using multivariat
229 Models
were adjusted for potential confounders and energy misre
230 Cox proportional hazards models
were adjusted for potential confounders.
231 Analyses
were adjusted for potential confounders.
232 a range of other known ADPKD manifestations
were adjusted for potential confounders.
233 Analyses
were adjusted for potential confounding due to age, sex,
234 Rate ratios
were adjusted for potential confounding variables.
235 Logistic regression models
were adjusted for potential demographic confounders and
236 Outcomes
were adjusted for potential sociodemographic, maternity,
237 When the FCAT test scores
were adjusted for potentially confounding maternal and i
238 ed for both sets of samples, and comparisons
were adjusted for potentially confounding variables.
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 Bone results
were adjusted for race, body mass index (BMI), and type
243 Statistical models
were adjusted for race, sex, smoking, body mass index, a
244 accounted for correlated interpretations and
were adjusted for reader-specific volume, two versions (
245 Multivariable logistic regression
was adjusted for relevant demographic characteristics.
246 Models
were adjusted for relevant child- and county-level chara
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 using motion-mode measurements and have not
been adjusted for sex or age.
255 This
was adjusted for sex, geographical region, and birth per
256 d, saturated, polyunsaturated, and total fat
were adjusted for sex and calories and divided into quin
257 alyzed with survival analysis techniques and
were adjusted for sex, age, calendar period, cohabitatio
258 All survival analyses
were adjusted for sex, age, calendar year, parental age,
259 Models
were adjusted for sex, FEV1, COPD status, age, body mass
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 All analyses
were adjusted for shared environment by means of the soc
264 lues were inverse normalized, and all traits
were adjusted for significant covariate effects of age a
265 Group comparisons
were adjusted for significant covariates.
266 Relative risk (RR) estimates
were adjusted for site, receipt of another vaccine durin
267 Models
were adjusted for socio-economic development and wider h
268 s from multivariate linear regression models
were adjusted for sociodemographic characteristics and f
269 Analyses
were adjusted for sociodemographic characteristics, heal
270 All analyses
were adjusted for sociodemographic data, vascular risk f
271 Regression models
were adjusted for sociodemographic factors and medical a
272 ts of physical activity on mortality and CVD
were adjusted for sociodemographic factors and other ris
273 egression models assessed yearly changes and
were adjusted for study center, race/ethnicity, gestatio
274 model in which the impact of each covariate
was adjusted for that of all others.
275 es from the survey for past periods can then
be adjusted for the estimated bias.
276 The methods applied here to mice can
be adjusted for the study of similarly prepared human lu
277 Propensity-matched analysis
was adjusted for the nonrandomized use of the 2 strategi
278 than -950 Hounsfield units on cardiac CT and
was adjusted for the number of total imaged lung voxels.
279 il size was measured in darkness and results
were adjusted for the baseline pupil and gender.
280 gression, our estimates of attributable risk
were adjusted for the demographic characteristics of the
281 Analyses
were adjusted for the following potential confounders: a
282 Estimates
were adjusted for the presence of comorbidities and are
283 Analyses
were adjusted for the prognostic stage, size, grade, and
284 on analyses were performed, and all analyses
were adjusted for the survey design.
285 In time-dependent models that
were adjusted for the use of a lipid-lowering medication
286 equent subgraph mining algorithms that could
be adjusted for this problem.
287 MRS and pathology associations
were adjusted for time from scan to death.
288 All models
were adjusted for time trend, season, influenza, and smo
289 All models
were adjusted for time.
290 All models
were adjusted for total energy intake, age, body mass in
291 ain magnetic resonance imaging outcomes also
were adjusted for total intracranial volume.
292 Models
were adjusted for traditional CVD risk factors.
293 Estimated hazard ratios (HRs)
were adjusted for transmission risk group, sex, age, yea
294 Analyses
were adjusted for urinary creatinine level, age, sex, et
295 ear all-cause mortality, and survival models
were adjusted for variables that confounded the chloride
296 tic thrombophilia, and procoagulant markers)
were adjusted for when comparing patients with RDD contr
297 Analysis
was adjusted for whether women had been age-eligible for
298 Analyses
were adjusted for within family correlation.
299 Estimates of sodium and potassium intakes
were adjusted for within-individual day-to-day variation
300 Analyses
were adjusted for year of birth (ie, partially adjusted)