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Clinical machine learning applications are often plagued with confoundersthat can impact the generalizability and predictive performance of thelearners. Confounding is especially problematic in remote digital healthstudies where the participants self-select to enter the study, thereby makingit challenging to balance the demographic characteristics of participants. Oneeffective approach to combat confounding is to match samples with respect tothe confounding variables in order to balance the data. This procedure,however, leads to smaller datasets and hence impact the inferences drawn fromthe learners. Alternatively, confounding adjustment methods that make moreefficient use of the data (e.g., inverse probability weighting) usually rely onmodeling assumptions, and it is unclear how robust these methods are toviolations of these assumptions. Here, rather than proposing a new approach tocontrol for confounding, we develop novel permutation based statistical methodsto detect and quantify the influence of observed confounders, and estimate theunconfounded performance of the learner. Our tools can be used to evaluate theeffectiveness of existing confounding adjustment methods. We illustrate theirapplication using real-life data from a Parkinson's disease mobile health studycollected in an uncontrolled environment.

Using permutations to assess confounding in machine learning applications for digital health