Specifically, due to the need to evaluate both the efficacy of more than one dose and potential equality of the efficacy between doses, we propose to extend the nonlocal alternative priors of Johnson and Cook [ 15] to those for multiple composite hypotheses with multivariate ordered parameters (such as priors in Hypotheses H2 and H3 in Section "Bayesian hypothesis testing").
Multivariate analysis of parameters such as age, pathological classification, FIGO staging, differentiation and Ki-67 expression was carried out using Cox's proportional hazard model.
The performance of the models were evaluated against the multivariate regression model using four different types of statistical parameters such as root mean square error (RMSE), coefficient of determination (R2), mean absolute error percentage (MAPE), and variables accounted for (VAF).
Multivariate analysis was effectively used to assess correlations among parameters such as OSCC, the genotypes, age and sex.
To compare the prognostic relevance of MSX2 expression with those of well-established histopathological parameters, such as T-stage and ulceration, multivariate Cox regression analysis was applied.
To evaluate prognostic values, we performed univariate and multivariate Cox-regression survival analysis in terms of MRS parameters such as Cho, Cr, NAA, MI, NAA/Cr, Cho/Cr, Cho/NAA, and MI/Cr.
In MCM, model calibration becomes analogous to coefficient estimation in conventional multivariate regression, and can be used to estimate poorly known parameters such as stoichiometric coefficients, growth kinetics or extracellular transport coefficients (MCM user manual, Supplementary file 1, section 12).
Many of these markers were independent of clinical parameters, such as age, FIGO stage or response to treatment, when multivariate analysis was performed.
Multivariate models were used to predict the relationship between the capillary parameters and other parameters such as exposure time and time after exposure.
The optimization of critical method parameters, such as the SPD reaction, was achieved using statistical experimental design and multivariate data analysis.
