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Both logistic regression and Bayesian models (to minimize type-1 errors) were used, adjusted for potential confounders.
The time-stratified control sampling approach was used, adjusted for particulate matter (PM10) concentrations, time trends and meteorological influences.
Unconditional logistic regression in successive models was used, adjusted for socio-demographic variables and other confounders.
In addition, specific diagnostic algorithms can be used, adjusted for age or developmental level [ 3, 15].
For analyses, repeated-measures analysis of variance was used, adjusted for covariates.
To adjust for confounding factors, a multiple logistic regression analysis was used, adjusted for age, sex, and comorbidity.
Similar(49)
Analysis of covariance models with baseline clinical measurement group as the main effects in the model were used, adjusting for age and gender.
23 Poisson regression was used, adjusting for propensity score deciles.
Also, the logarithm of the dependent variable can be used adjusting for skewness.
Unconditional logistic regression was used, adjusting for age, sex, and when appropriate housing characteristics.
Linear mixed models were used adjusting for age, gender, social support and family status.
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