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Would make sense to apply any oversampling (e.g. SMOTE et similia) techniques in order to balance the outcome classes in the context of longitudinal/panel data? Wouldn't such procedures ignore the correlation amongst observations (e.g. in a classic design of a patient observed for 2 or more time points) might just introduce more noise in the analysis synthesizing new observations at random time points-wise?

Thanks a lot in advance for your support.

joosefupas
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    Welcome to Cross Validated! Statisticians do not see class imbalance as such a problem, and there is no need to use artificial balancing to solve a non-problem. It might be helpful if you say why you find the imbalance problematic. https://stats.stackexchange.com/questions/357466 https://www.fharrell.com/post/class-damage/ https://www.fharrell.com/post/classification/ https://stats.stackexchange.com/a/359936/247274 https://stats.stackexchange.com/questions/464636/ https://twitter.com/f2harrell/status/1062424969366462473?lang=en – Dave Mar 03 '22 at 10:47
  • Related to Dave's excellent comment you have fallen into a very common machine learning trap of wrongly conceptualizing a sensible analyses of tendencies to get a disease (probability of disease) as a forced-choice premature decision classification problem. Medical diagnosis and prognosis are areas for which classification is singularly unhelpful, and tendencies are everything. – Frank Harrell Mar 03 '22 at 12:36

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