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This situation may also arise in clinical trials when interactions occur between treatment effects and covariates, or when adjustment is needed for prognostic factors that are unbalanced between groups.

Thus the random-effects framework needs to accommodate these covariates during modeling in order to estimate factor-outcome associations after adjusting for other factors. Factors that are strongly associated with the outcome might retain their association even when adjusting for other variables.