Sentence examples for model that corrected from inspiring English sources

The strength of associations between HLA class I alleles and viral polymorphisms in the "total" cohort, "high CD4" group or "low CD4" group was derived using a logistic regression model that corrected for phylogeny, and is reported as a log2-adjusted odds ratio.

The conclusions were unchanged in a subsequent model that corrected for pre-specified confounders.

In order to address this, we used a Cox proportional hazard regression model that corrected for all available influential covariates.

To determine the association, an ordered multinomial logistic regression model that corrected for clustering within individual providers and patients and adjusted for patient and encounter characteristics was utilized.

Similarly, in the CV model that corrected for hypoglycemia, CV of glucose and hypoglycemia were no longer independent predictors of mortality, whereas age, BMI, and admission to the medical service remained significant predictors.

In the CV model that corrected for age, BMI, and hospital service, but not for hypoglycemia, however, CV remained significantly associated with increased 90-day mortality, which rose by 14% with each 10 percentage point increment in CV of glucose (RR 1.14 [1.01–1.29], P = 0.037).

Analysis was conducted using spatial stochastic frontier models that correct for heterogeneity and spatial interactions between sub-national units.

Linear mixed models that correct for confounding by the genetic background using a kinship matrix calculated from genetic data were used throughout (Kang et al., 2010; Segura et al., 2012).

In future, it may be feasible to develop universal models that correct for sensitivity/specificity differentials of the methods used to test for infection, preferably based on large-scale population surveys which have used both RDT and microscopy for the same individuals with the appropriate quality assurance and external validity.

Understanding the accuracy of self-reported data and developing prediction models that correct for underreporting of hypertension in self-reported data can be critical tools in the development of more accurate population level estimates, and in planning population-based interventions to reduce the risk of, or more effectively treat, hypertension.

Assessing the validity of self-reported data in estimating hypertension prevalence in specific geographic areas, and developing simple prediction models that correct for possible miss reporting of HBP in self-reported data, can be essential to the creation of accurate population level estimates, and for population level efforts to effectively prevent or treat HBP within particular contexts.