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This is especially true when multiple factors are considered in a multivariate analysis, where one can easily over-fit the data.
The IFN-γ decrease in keratoconus compared to controls was significant by Mann-Whitney testing (p = 0.02, analysis not shown), but not by multivariate analysis, where the large variance of the measurements appeared to diminish its significance (Table 3).
Gender, baseline GCS, cranial nerve palsy and hemiparesis on presentation went forward into multivariate analysis where only hemiparesis remained significant in the final model (OR = 0.05, p = 0.04 [95% CI 0.003, 0.92]).
Confounding is addressed through the use of multivariate analysis, where the sample size is adequate.
This should include a multivariate analysis where several biases should be taken into consideration.
Analysis will also include categorical analysis, factor analysis and bivariate and multivariate analysis where relevant.
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This table displays results from a multivariate sensitivity analysis where discount rate, useful life and input cost were varied.
This was followed by a multivariate regression analysis where independent variables that showed a significant effect during univariate analysis were included.
A multivariate sensitivity analysis, where estimates of provider cost and effectiveness adjusted by the death rate are varied at the same time, was also performed.
Based on these findings, the variables trust in others and bridging trust were chosen as determinants of sexual behaviors in a multivariate logistic analysis where we adjusted for potential confounding factors.
This finding was confirmed in the multivariate adjusted analysis where the odds of patent infection was reduced by ≥ 80% and the odds of symptomatic infection was reduced by ≥ 66.7% in infected adults compared with infected children.
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