Speaker
Description
In regression modeling, relationships between continuous predictors and outcomes are often assumed to be linear, yet allowing for non-linear associations can substantially improve model performance. A variety of methods for flexible regression—such as fractional polynomials and spline-based approaches—have been proposed to model non-linear associations. However, comprehensive and systematic simulation studies comparing multiple flexible regression techniques remain scarce. Such comparisons are essential for guiding researchers in selecting appropriate methods in different data settings.
We present results from a simulation study that systematically compares several flexible regression approaches. A central feature of our study design is the careful selection of performance measures used to assess the curves estimated by the different methods. Different measures may capture different aspects of the curves and therefore favor different methods. This was recently illustrated in our publication on a categorization of performance metrics for evaluating non-linear associations between continuous predictors and outcomes [1], published on behalf of Topic Group 2 of the Strengthening Analytical Thinking in Observational Studies (STRATOS) initiative. Because the categorization includes a wide range of measures, we propose a novel strategy to identify subsets of measures that capture distinct aspects of the estimated curves. Our "clustered-metric strategy" is based on cluster analysis of the performance measures to detect groups of measures that attribute similar performance to the methods. By means of applying our clustered-metric evaluation to the results of our simulation study, we demonstrate that this approach may reduce redundancies and facilitate a clearer interpretation of the methods' performance while avoiding selective reporting.
Our proposed clustered-metric evaluation illustrates how different performance measures align – or diverge – in assessing model quality. It is a transparent and concise strategy to report results of a comprehensive simulation study, well suited for comparing flexible regression techniques, but also transferable to other topics.
[1] Ullmann, T., Heinze, G., Abrahamowicz, M., Perperoglou, A., Sauerbrei, W., Schmid, M., Dunkler, D., for TG2 of the STRATOS Initiative, 2025. A Systematic Categorization of Performance Measures for Estimated Non‐Linear Associations Between an Outcome and Continuous Predictors. Wiley Interdisciplinary Reviews: Computational Statistics, 17(3), e70042.
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