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Partial least squares method includes one procedure which is similar to principal component analysis (PCA), therefore can be used on variables with multiple correlations.
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These methods were used on variables from each technology and all combined variables from both technologies.
The Kolmogorov-Smirnov test analyzed the distribution of the numerical variables and, when necessary, logarithm transformation was used on variables with non-parametric distribution.
Descriptive analysis was used on variables such as age at diagnosis, serum PSA (pre- and post-relapse), Gleason sum, time to biochemical relapse, time to death from biochemical relapse and overall survival.
Prolongation and restriction can be used on conservative variables or primitive variables.
McNemar's test will be used on the categorical variables, with a level of significance for the p value fixed at <0.05.
Around 90% of patients did not use any mental health services, therefore neither a t-test, nor a non-parametric test such as Kruskall Wallis, could be used on the continuous variable "number of contacts", hence the variable was dichotomised.
Step-down p-value adjustment was used on S1 variables.
After imputation, sensible rounding [ 20] was used on these variables to prepare the data for analysis.
PCA was used on the variables and two components were found that are now used as the Pampalon indices: Values for the Material and Social components.
To explore the huge design space, genetic algorithm are used on a 4 variables application case and the results are presented through a parallel graph.
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