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Statistical learning (SL) includes methods that extract knowledge fromcomplex data. SL methods beyond generalized linear models are beingincreasingly implemented in public health research and epidemiology becausethey can perform better in instances with complex or high-dimensionaldata---settings when 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 SL models that have been applied previously to analyze therelationship between environmental mixtures and health outcomes. We ran eachmodel across 100 initializing values for random number generation, or "seeds,"and assessed variability in resulting estimation and inference. All methodsexhibited some seed-dependent variability in results. The degree of variabilitydiffered across methods and exposure of interest. Any SL method reliant on arandom seed will exhibit some degree of seed sensitivity. We recommend thatresearchers repeat their analysis with various seeds as a sensitivity analysiswhen implementing these methods to enhance interpretability and robustness ofresults.

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