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Weights were determined using multiple logistic regression with independent demographic variables selected based on a "Super Learner" algorithm using V-fold cross-validation (van der Laan et al. 2007).
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The super learner also had a better area under the receiver operating characteristic curve (0.79) than existing post-disaster PTSD risk tools.
Finally, a weighted linear combination of the candidate learners is used to build a new estimator, the so-called super learner estimator [ 15].
The super learner weighted estimator achieved an estimated AUROC of 0.760 955% CI 0.694, 0.826).
The super learner algorithm had better cross-validated performance than the 39 individual algorithms from which it was developed, including conventional logistic regression.
The discrete super learner has been proposed by Dudoit and Van der Laan et al. [ 13] as a generalization of the stacking algorithms [ 14], to choose the optimal regression algorithm among a set of candidates.
We analyzed the data using SPSS (version 20.0; IBM Corp., Somers, NY) for bivariate analyses, Stata (version IC11.2; StataCorp, College Station, TX) for GEE models, and R (v.2.14.2; R Foundation for Statistical Computing, Vienna, Austria) for Super Learner models.
For each candidate algorithm, the super learner then averages the V estimated risks over the V validation sets, resulting in the so-called cross-validated risk, for each candidate algorithm.
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