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Two recent studies have explored prognostic factors for outcome of radiculopathy using a multivariable approach.
Variables found to have no association using a multivariable approach were also taken into account.
We only included cohort studies using a multivariable approach for data analysis to correct for confounding.
In our study we discuss further this topic and illustrated how the methodology should be used when investigating other serious events in dialysis populations using a multivariable approach.
Further, cohort studies that failed to evaluate risk factors using a multivariable approach were excluded given the multitude of possible confounding variables.
Studies that developed and validated an ADR prediction model for use in patients over 65 years old, using a multivariable approach in the design and analysis, were included.
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We included RCTs as well as cohort studies that used a multivariable approach for data analysis.
Two models were fitted, the first one controlling for age, the second model uses a multivariable approach with age, BMI, education, family status, physical activity, smoking, alcohol consumption, fasting blood glucose levels, total cholesterol and triglycerides as potential confounder.
Two previous studies on LBP have used a multivariable approach to exploring the relationship between MRI findings and clinical variables, but in contrast to the current study, their multivariable analysis clustered the clinical variables and related these to single MRI findings.
Indirect standardization develops risk adjustment using a multivariable regression approach.
Moreover, since differential recall of detailed physical activity history between cases and controls might occur, we conducted case case comparisons for ER− versus ER+, PR− versus PR+, HER2− versus HER2+, and p53− versus p53+ patients using a multivariable unconditional logistic regression approach 48.
More suggestions(15)
using a multivariable analysis
using a modelling approach
using a personalised approach
using a direct approach
using a quantitative approach
using a multivariable model
using a different approach
using a multiantenna approach
using a bootstrap approach
using a multivariable control
using a separate approach
using a contrarian approach
using a phylogenomic approach
using a multivariable linear
using a multivariable regression
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