Estimation in regret-regression using quadratic inference functions with ridge estimator

In this paper, we propose a new estimation method in estimating optimal dynamic treatment regimes. The quadratic inference functions in myopic regret-regression (QIF-MRr) can be used to estimate the parameters of the mean response at each visit, conditional on previous states and actions. Singularity issues may arise during computation when estimating the parameters in ODTR using QIF-MRr due to multicollinearity. Hence, the ridge penalty was introduced in rQIF-MRr to tackle the issues. A simulation study and an application to anticoagulation dataset were conducted to investigate the model's performance in parameter estimation. The results show that estimations using rQIF-MRr are more efficient than the QIF-MRr.

Automated summary

In this paper, a new estimation method is proposed for estimating optimal dynamic treatment regimes. The quadratic inference functions in myopic regret-regression (QIF-MRr) are used to estimate the parameters of the mean response at each visit. Singularity issues may arise when estimating the parameters in ODTR using QIF-MRr due to multicollinearity. Hence, the ridge penalty was introduced in rQIF-MRr to tackle the issues. Simulation study and application to anticoagulation dataset show that estimations using rQIF-MRr are more efficient than the QIF-MRr.