Table 5 presents the univariate and multivariate regression estimates for the best variable within each factor.
Note the univariate and multivariate regression estimates changed due to the correlations between the variables included in the regression.
However, our multivariate regression estimates should be interpreted with caution due to their high correlation with availability of on-site cardiac catheterization.
Accounting for the variable time and for multiple factors potentially affecting their occurrence, we used Cox proportional hazards multivariate regression to estimate adjusted hazard ratios (aHR), and their 95% confidence intervals (CI) for risk factors for serious AEs.
95%% confidence intervals (CIs) for coefficients in bivariate and full multivariate regression were estimated using the bootstrap (as an agnostic estimation method that does not rely on model form assumptions) [ 36, 37].
Multivariate regression models estimated the extent to which retrospective circumstances were independently associated with adult educational attainment and adult health.
Table 4 presents the results of the multivariate regression models estimating the effect of comorbidities on health care costs.
Using multivariate regression, we estimated the reduction in mortality that was associated with the allocation of additional NHS resources in these areas.
However, both these approaches are less accurate than testing the significance of the model using a Wald test based on the combined multivariate regression parameter estimates (Table 2(B)).
Table 3 shows multivariate regression models estimating the associations between use of GP services indicated by FFS, patient, and GP characteristics, with separate analyses for patients with schizophrenia, DM, and the total population.
