There are several methods for building prediction models. The wealth of currently available modeling techniques usually forces the researcher to judge, a priori, what will likely be the best method. Super learning (SL) is a methodology that facilitates this decision by combining all identified prediction algorithms pertinent for a particular prediction problem. SL generates a final model that is at least as good as any of the other models considered for predicting the outcome. The overarching aim of this work is to introduce SL to analysts and practitioners. This work compares the performance of logistic regression, penalized regression, random forests, deep learning neural networks, and SL to predict successful substance use disorders (SUD) treatment. A nationwide database including 99,013 SUD treatment patients was used. All algorithms were evaluated using the area under the receiver operating characteristic curve (AUC) in a test sample that was not included in the training sample used to fit the prediction models. AUC for the models ranged between 0.793 and 0.820. SL was superior to all but one of the algorithms compared. An explanation of SL steps is provided. SL is the first step in targeted learning, an analytic framework that yields double robust effect estimation and inference with fewer assumptions than the usual parametric methods. Different aspects of SL depending on the context, its function within the targeted learning framework, and the benefits of this methodology in the addiction field are discussed.
In Argentina, there is no information on ages of attainment of developmental milestones and very few data about environmental factors that influence them. A national survey on the psychomotor development of children under 6 years of age was carried out with the help of 129 paediatricians. Logistic regression was applied to a final sample of 3573 healthy, normal children in order to estimate selected centiles (25th, 50th, 75th and 90th), together with their respective confidence intervals, of the ages of attainment of 78 developmental items belonging to the following areas: personal-social (18 items), fine motor (19), language (18) and gross motor (23). The 50th centile obtained for each of the 43 comparable items was compared with those obtained in previously standardised tests: DDST, Denver II, Bayley and Chilean scales. Neither significant nor systematic differences were found between our results and those described in the tests used for comparison. Multiple logistic regressions showed that social class, maternal education and sex (female) were associated with earlier attainment of some selected developmental items, achieved at ages later than 1 year. Selected items achieved before the first year of life were not affected by any of the independent environmental variables studied. The information is useful in helping paediatricians in their daily practice for surveillance of development, as baseline information for epidemiological studies on development in our country and for cross-cultural analysis.
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