1 Logistic regression
models were adjusted for a broad range of potential confoundi
2 All
models were adjusted for a minimum of 18 a priori determined
3 Models were adjusted for age and conditioned on calendar day
4 High-dimensional statistical analyses were performed, all
models were adjusted for age and smoking, and p-values were a
5 Generalized linear
models were adjusted for age, age squared, sex, height, princ
6 Multivariable
models were adjusted for age, gender, race, diagnosis, centra
7 Models were adjusted for age, race or ethnicity, smoking, hep
8 Survival
models were adjusted for age, sex, alcohol intake, smoking hi
9 Models were adjusted for age, sex, and BMO area.
10 All
models were adjusted for age, sex, ethnicity, and waist circu
11 Models were adjusted for age, sex, parasitemia, inflammation,
12 Models were adjusted for age, sex, race/ethnicity, education,
13 Models were adjusted for age, sex, race/ethnicity, education,
14 Regression
models were adjusted for age, sex, season, and pubertal stage
15 Models were adjusted for age, years enrolled, parity, and rac
16 Final multivariate
models were adjusted for age.
17 Models were adjusted for baseline characteristics and severit
18 Regression
models were adjusted for baseline function and patient and tu
19 Models were adjusted for calendar time and other potential co
20 Cox proportional hazards regression
models were adjusted for cardiovascular disease risk factors.
21 Models were adjusted for confounders, including other Healthy
22 Multivariate
models were adjusted for covariates (age, sex, tumor grade, T
23 Cox proportional hazards
models were adjusted for demographic and cardiovascular risk
24 Models were adjusted for demographics, viral loads, CD4 count
25 Logistic regression
models were adjusted for education, other early life characte
26 Models were adjusted for estimated cell type proportions, age
27 Two-pollutant
models were adjusted for fine particles with aerodynamic diam
28 Models were adjusted for health and lifestyle factors, dietar
29 Regression
models were adjusted for individual sociodemographic and clin
30 Models were adjusted for individual, maternal, and household
31 All
models were adjusted for individual-level predictors includin
32 Models were adjusted for inverse probability of sampling weig
33 All
models were adjusted for maternal age, education, annual hous
34 20 to 1.48), the difference was no longer evident after the
models were adjusted for mistreatment (odds ratio, 0.90; 95%
35 Multivariable linear probability
models were adjusted for patient and hospital characteristics
36 All
models were adjusted for patient and hospital characteristics
37 All
models were adjusted for patient demographics, comorbidities,
38 Linear mixed
models were adjusted for postpartum age and infant sex.
39 Models were adjusted for potential confounders and energy mis
40 All
models were adjusted for potential confounders, including dem
41 Models were adjusted for relevant confounders.
42 Separate
models were adjusted for screen-detected and interval cancers
43 Models were adjusted for sex, age, education, and income (tot
44 Models were adjusted for sex, age, education, baseline test s
45 Models were adjusted for socio-economic development and wider
46 All
models were adjusted for sociodemographic, criminographic, an
47 Models were adjusted for sociodemographics, cardiovascular di
48 Models were adjusted for socioeconomic, health, and demograph
49 Models were adjusted for traditional risk factors, low-densit
50 Models were adjusted for within-ICU correlation, patient- and