  Individualized treatment rules (ITRs) for treatment recommendation is an
important topic for precision medicine as not all beneficial treatments work
well for all individuals. Interpretability is a desirable property of ITRs, as
it helps practitioners make sense of treatment decisions, yet there is a need
for ITRs to be flexible to effectively model complex biomedical data for
treatment decision making. Many ITR approaches either focus on linear ITRs,
which may perform poorly when true optimal ITRs are nonlinear, or black-box
nonlinear ITRs, which may be hard to interpret and can be overly complex. This
dilemma indicates a tension between interpretability and accuracy of treatment
decisions. Here we propose an additive model-based nonlinear ITR learning
method that balances interpretability and flexibility of the ITR. Our approach
aims to strike this balance by allowing both linear and nonlinear terms of the
covariates in the final ITR. Our approach is parsimonious in that the nonlinear
term is included in the final ITR only when it substantially improves the ITR
performance. To prevent overfitting, we combine cross-fitting and a specialized
information criterion for model selection. Through extensive simulations, we
show that our methods are data-adaptive to the degree of nonlinearity and can
favorably balance ITR interpretability and flexibility. We further demonstrate
the robust performance of our methods with an application to a cancer drug
sensitive study.
