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Model selection was done based on backward selection and ANOVA tests.
We then further reduced the model using a stepwise Poisson regression (with log-link) based on backward selection and the BIC-criterion [ 19] and finally applied the weighted GEE analysis.
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The elimination of non-significant variables in regression analysis based on backward variable selection is similar to the pruning of the tree that contains the maximum number of terminal nodes.
The final model (third column) is based on backward elimination of non-significant factors.
Further selection of variables was based on backward elimination procedure using a LR-test at 0.05 as cut-point.
Both methods yielded the same result and therefore only results based on backward elimination are presented.
They are based on a backward selection process, and the models comprise complete cases only (N = 7,198).
The most appropriate (log or FP) transformation, if any, was applied to each variable and all variables were considered multivariately using Cox proportional hazards regression based on a backward selection method using a nominal significance level of 0.05 for elimination and including trial, sex, cancer stage (stratification factors at randomisation) and randomised treatment in each model.
Variable selection for the final multivariate model was therefore based on a manual backward selection procedure (alpha level of 0.05) in which each variable was removed in turn starting with the variable with the highest p-value.
This algorithm determines the ranking of the features based on a backward sequential selection method that removes one feature at a time.
Our implementation operates in up- and down-swing phases which are based on Sequential Forward Selection (SFS) and Sequential Backward Selection (SBS).
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