  Patient care may be improved by recommending treatments based on patient
characteristics when there is treatment effect heterogeneity. Recently, there
has been a great deal of attention focused on the estimation of optimal
treatment rules that maximize expected outcomes. However, there has been
comparatively less attention given to settings where the outcome is
right-censored, especially with regard to the practical use of estimators. In
this study, simulations were undertaken to assess the finite-sample performance
of estimators for optimal treatment rules and estimators for the expected
outcome under treatment rules. The simulations were motivated by the common
setting in biomedical and public health research where the data is
observational, survival times may be right-censored, and there is interest in
estimating baseline treatment decisions to maximize survival probability. A
variety of outcome regression and direct search estimation methods were
compared for optimal treatment rule estimation across a range of simulation
scenarios. Methods that flexibly model the outcome performed comparatively
well, including in settings where the treatment rule was non-linear. R code to
reproduce this study's results are available on Github.
