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Objectives: A conceptually oriented preprocessing of a large number of potential prognostic factors may improve the development of a prognostic model. This study investigated whether various forms of conceptually oriented preprocessing or the preselection of established factors was superior to using all factors as input.Study Design and Setting: We made use of an existing project that developed two conceptually oriented subgroupings of low back pain patients. Based on the prediction of six outcome variables by seven statistical methods, this type of preprocessing was compared with medical experts' preselection of established factors, as well as using all 112 available baseline factors.Results: Subgrouping of patients was associated with low prognostic capacity. Applying a Lasso-based variable selection to all factors or to domain-specific principal component scores performed best. The preselection of established factors showed a good compromise between model complexity and prognostic capacity. Conclusion:The prognostic capacity is hard to improve by means of a conceptually oriented preprocessing when compared to purely statistical approaches. However, a careful selection of already established factors combined in a simple linear model should be considered as an option when constructing a new prognostic rule based on a large number of potential prognostic factors.

Particle swarm optimization (PSO) is an iterative search method that moves a set of candidate solution around a search-space towards the best known global and local solutions with randomized step lengths. PSO frequently accelerates optimization in practical applications, where gradients are not available and function evaluations expensive. Yet the traditional PSO algorithm ignores the potential knowledge that could have been gained of the objective function from the observations by individual particles. Hence, we draw upon concepts from Bayesian optimization and introduce a stochastic surrogate model of the objective function.That is, we fit a Gaussian process to past evaluations of the objective function, forecast its shape and then adapt the particle movements based on it. Our computational experiments demonstrate that baseline implementations of PSO (i. e., SPSO2011) are outperformed. Furthermore, compared to, state-of-art surrogate-assisted evolutionary algorithms, we achieve substantial performance improvements on several popular benchmark functions. Overall, we find that our algorithm attains desirable properties for exploratory and exploitative behavior.