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We adjusted for both confounders (by using residualised QALYs), since they were significant predictors of the QALY end-term,.
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We performed logistic regression analysis to assess the relation between quintiles of NAEes and albuminuria, and eGFR <60 mL/min per 1.73 m, both individually and with the inclusion of potential confounders by using the lowest quintile category as the referent.
Though these studies tried to adjust the confounders by using multivariate logistic regressions, there are still residual confounding factors [ 39].
We were able to adjust for a large number of confounders by using propensity scores; this approach can successfully account for much confounding by indication.
The basic bootstrap method, which did not adjust for confounders, was compared with two model-based approaches, which adjust for confounders by using regression models.
We attempted to control for confounders by using regression analysis.
ORs were adjusted for possible confounders by using a step-up logistic regression model.
This method creates and prioritizes potential confounders by using all available diagnostic codes, procedural codes, hospital admissions, drugs, and laboratory values.
Third, although we have tried our best to control bias and confounders by using multivariate model, many other known or unknown factors may still exist.
Variables that failed to meet screening criteria were assessed as potential confounders by using β estimate changes of >15% as the criteria.
Both plasma and urine NGAL, Cys-C, and sTREM-1 were significantly associated with AKI development in patients with sepsis, even after adjustment for confounders by using generalized estimating equations.
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