The joint modeling approach is a statistical technique used to simultaneously analyze multiple related outcomes, particularly when one of the outcomes is subject to missing data or measurement error. This method is crucial in various applications, allowing researchers to make better inferences by capturing the relationships between different variables while properly handling incomplete data through imputation.
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