Journal of Data Science

Precision medicine is an innovative approach that aims to customize medical treatments and interventions to patients based on their individual characteristics. Several estimation techniques, including Q-learning, have been developed to determine optimal treatment rules. However, the applicability of these methods depends on the availability of precisely measured variables. This study extends the scope of Q-learning to incorporate compound outcomes, deviating from the commonly assumed univariate outcomes, and further accommodates data with mismeasurement in both binary and continuous covariates. Two methods are described to mitigate the impact of mismeasurement. Numerical studies reveal that mismeasurement in covariates leads to notable estimation bias in parameters indexing the optimal treatment, yet the methods addressing the mismeasured effects yield improved results.