1 Logistic regression
models were adjusted for a broad range of potential confoundi
2 Models were adjusted for a stroke or CHD risk score and behav
3 Models were adjusted for age and ethnicity (Ashkenazi Jewish
4 All
models were adjusted for age and sex.
5 High-dimensional statistical analyses were performed, all
models were adjusted for age and smoking, and p-values were a
6 ed linear models were developed to identify predictors, and
models were adjusted for age at diagnosis, sex, and parent ed
7 All
models were adjusted for age at time of scan, gender, ethnici
8 Cox regression
models were adjusted for age, AF risk factors, inflammatory,
9 Multivariate Cox regression
models were adjusted for age, education, smoking, physical ac
10 Multivariate Cox regression
models were adjusted for age, family history of hypertension,
11 Models were adjusted for age, income, smoking status, frequen
12 Models were adjusted for age, principal diagnosis, and comorb
13 Models were adjusted for age, race or ethnicity, smoking, hep
14 Cox proportional hazards
models were adjusted for age, race, education, body mass inde
15 Survival
models were adjusted for age, sex, alcohol intake, smoking hi
16 Models were adjusted for age, sex, and BMO area.
17 All
models were adjusted for age, sex, ethnicity, and waist circu
18 Models were adjusted for age, sex, height, weight, pack-years
19 Models were adjusted for age, sex, race/ethnicity, education,
20 Regression
models were adjusted for age, sex, season, and pubertal stage
21 Linear
models were adjusted for age, sex, years of education, and ap
22 Models were adjusted for age, years enrolled, parity, and rac
23 Final multivariate
models were adjusted for age.
24 Models were adjusted for calendar time and other potential co
25 Cox proportional hazards regression
models were adjusted for cardiovascular disease risk factors.
26 Multivariable
models were adjusted for child age, sex, race/ethnicity, and
27 Multivariable
models were adjusted for comorbidity status (incidental vs co
28 The
models were adjusted for confounders such as body size.
29 Models were adjusted for country fixed effects, survey-year f
30 Multivariate
models were adjusted for covariates (age, sex, tumor grade, T
31 Cox proportional hazards
models were adjusted for demographic and cardiovascular risk
32 Multivariable-adjusted repeated measure logistic regression
models were adjusted for demographic characteristics, clinica
33 Logistic and linear regression
models were adjusted for demographic, lifestyle, and dietary
34 Linear regression
models were adjusted for demographics, anthropometrics, smoki
35 Models were adjusted for demographics, behaviors, and physiol
36 Cox regression was used, and
models were adjusted for important baseline and clinical cova
37 Models were adjusted for individual, maternal, and household
38 Models were adjusted for inverse probability of sampling weig
39 Logistic regression
models were adjusted for maternal age, race, education, body
40 All
models were adjusted for patient and hospital characteristics
41 All
models were adjusted for patient demographics, comorbidities,
42 Models were adjusted for potential confounders and energy mis
43 Logistic regression
models were adjusted for potential demographic confounders an
44 Statistical
models were adjusted for race, sex, smoking, body mass index,
45 Separate
models were adjusted for screen-detected and interval cancers
46 Models were adjusted for socio-economic development and wider
47 Results from multivariate linear regression
models were adjusted for sociodemographic characteristics and
48 All
models were adjusted for total energy intake, age, body mass
49 Models were adjusted for traditional CVD risk factors.
50 were followed for 5-year all-cause mortality, and survival
models were adjusted for variables that confounded the chlori