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Optimal individualized treatment decision-making has improved health outcomesin recent years. The value function is commonly used to evaluate the goodnessof an individualized treatment decision rule. Despite recent advances,comparing value functions between different treatment decision rules orconstructing confidence intervals around value functions remains difficult. Wepropose a t-test based method applied to a test set that generates validp-values to compare value functions between a given pair of treatment decisionrules when some of the data are missing. We demonstrate the ease in use of thismethod and evaluate its performance via simulation studies and apply it to theChina Health and Nutrition Survey data.

Constructing a T-test for Value Function Comparison of Individualized Treatment Regimes in the Presence of Multiple Imputation for Missing Data