We further examined the effects in multivariable models of adding conventional risk factors [baseline smoking category (never/ex/current), years of education, plasma triglyceride levels and systolic blood pressure].
This model consists of adding a lag window period which, in our study, was defined according to the biologic agent used.
To assess the statistical significance of differences in sensitivity and specificity between tests, we compared the fit of alternative models (effect of adding or removing covariate terms from the model) by using likelihood ratio tests.
Having developed the general model, we then discuss the models scalability in terms of adding new protocols, categories, requirements, and performance criteria.
First, a probit model of the probability of adding SO2 to the wine is estimated.
This addition to the model had the effect of adding additional noise to p53 synthesis.
Net reclassification improvement (NRI) quantifies improvement in the model as a result of adding one or more new biomarkers.
We therefore modelled only the impact of adding angiotensin blockade.
Finally, the MIP offers the possibility of adding further model constraints in case these are required from a practical perspective.
Using the model, we explored the effects of adding a phosphatase prior to testing in vivo.
