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Results were similar for the bivariate models and for the first multivariate model whether we used complete or imputed data.
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The variables were initially assessed by bivariate analysis and retained in the multivariate models whether or not they were statistically significant.
Each multivariate model examined whether the product term (intervention X characteristic), was significant (p < .10).10
The interaction term P value of the multivariate model assesses whether a change from baseline to endline differed significantly between the intervention and comparison groups.
Finally, we also added the variables age and gender to the final multivariate model independent of whether they showed any association with the outcome.
> -wrap-foot> Oddsdds ratio, 95% CI 95% confidence interval, ATC Anatomical Therapeutic Chemical, NA not applicable, OD orphan drug The results from the multivariate model, which assessed whether any of the statistically significant characteristics were related, are shown in Table 4.
We were also interested in the nature of bivariate associations within the multivariate model (e.g. whether life events had a stronger genetic correlation with depression or delinquency).
Table 4-wrap> summarises the best multivariate models obtained, whether they included only tumour markers, or CA125 and/or TPA along with a short list of standard prognostic factors (i.e., sex, age, performance status, and the TNM factors), or, finally, whether they included all the variables listed in Table 1.
For those variables, which showed an association with antepartum stillbirth in the univariate logistic regression analysis a multivariate model was created to determine whether the association between the potential risk factors and antepartum stillbirth remained after adjusting for confounders.
Several of the indicators were eliminated in the final multivariate model including household income (p = 0.533), whether ever homeless (p = 0.749), family composition (p = 0.433), main source of income (p = 0.638) and labour force status (p = 0.929).
As TACE cycle numbers were included into the multivariate model this would be applicable independently regardless whether the first or a later TACE treatment is applied.
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