Reflection on modern methods: good practices for applied statistical learning in epidemiology.

Abstract

Statistical learning includes methods that extract knowledge from complex data. Statistical learning methods beyond generalized linear models, such as shrinkage methods or kernel smoothing methods, are being increasingly implemented in public health research and epidemiology because they can perform better in instances with complex or high-dimensional data-settings in which traditional statistical methods fail. These novel methods, however, often include random sampling which may induce variability in results. Best practices in data science can help to ensure robustness. As a case study, we included four statistical learning models that have been applied previously to analyze the relationship between environmental mixtures and health outcomes. We ran each model across 100 initializing values for random number generation, or 'seeds', and assessed variability in resulting estimation and inference. All methods exhibited some seed-dependent variability in results. The degree of variability differed across methods and exposure of interest. Any statistical learning method reliant on a random seed will exhibit some degree of seed sensitivity. We recommend that researchers repeat their analysis with various seeds as a sensitivity analysis when implementing these methods to enhance interpretability and robustness of results.