In order to identify important covariates, multivariate logistic regression analyses (single step, forced entry) were conducted with target attainment 100% fT>MIC and target attainment 50% fT>MIC as dependent variable using the variables which gave a P value of <0.10 in the univariate analysis.
The index was to be calculated using the ratio of the number of deaths during admission to a particular hospital in a year compared with the number of expected deaths calculated using a model adjusted for the variables given in table 1.
To this end, a combined variable "Presence of errors potentially affecting the study results" was defined using the last four variables given in Table 4.
For the three concrete mixtures tested, the estimates using the constant value of E i produced the approximately same strength age curves as the estimates using the variable value E a given in Table 6.
The staining intensities reflected the number of immunopositive cells, thus conducting the analyses using this variable gave no significant associations not seen using the number of positive cells.
For prognostic/predictive analyses, PFS and OS were used as the dependent variables, given the expected low ORR.
Variables were treated as continuous, or with simple, for example, log transformations in the Cox models, unless analysis using optimum cut-points of the variable gave substantially larger χ values in the Cox model.
This last lemma allows the computation of the polynomials q and (q_) related to the polynomial RL on the variable D using the formula given in Remark 2.2.
Later, an experiment was run using the optimum values for variables given by response optimization to confirm the predicted value and cellulase production was confirmed.
Modelling the excess mortality rate ratios using ordinal independent variables gives good agreement with a similar, but categorical, model for colorectal cancer (Downing et al, 2013).
