Sentence examples for forward for multivariate analysis from inspiring English sources

Unadjusted analysis was performed and on the basis of this analysis, factors that had P values < 0.2 were carried forward for multivariate analysis.

Second, techniques such as forward or backward selection for multivariate analysis automatically reject nonsignificant results.

Variables that were significant on univariate analysis were incorporated into a linear regression model (forward, stepwise variable entry) for multivariate analysis of factors influencing the items to evaluate postoperative urinary and sexual QoL over time.

Symptoms that reached a significance level of 0.05 (and had greater than five expected numbers) were considered for multivariate analysis using forward stepwise regression.

Spearman correlations and Kruskal Wallis tests were used to examine univariate relationships between variables; but for multivariate analysis parametric forward stepwise multiple regression was used, in the absence of a suitable non-parametric alternative.

The Pearson and Spearman correlation analyses were used to determine the correlation coefficient, the Kaplan-Meier method was used to analyze survival, and the Cox proportional hazard regression model was used for multivariate analysis in forward stepwise regression.

Cox's proportional hazard model was used for multivariate analysis with forward-stepwise regression based on Wald's statistic.

These variables were selected for multivariate analysis using a forward stepwise method, and five indexes were identified to be independent prognostic factors for OS.

When the data were stratified for multivariate analysis using both forward and backward stepwise Cox regression procedures, YWHAZ immunoreactivity in tumour cells remained significant at P<0.05 (hazard ratio, 2.3 (1.003–5.304)) for overall survival in all patients, suggesting that immunoreactivity is an independent predictor of overall survival.

Variables for multivariate analysis were selected by means of a forward stepwise approach, using a significance level of p < 0.10 for entering into or remaining in the model.

For multivariate analysis, logistic regression according to the Wald method with forward stepwise selection (entry level 0.05; level for keeping the variable 0.051) was used.