  In biomedical science, analyzing treatment effect heterogeneity plays an
essential role in assisting personalized medicine. The main goals of analyzing
treatment effect heterogeneity include estimating treatment effects in
clinically relevant subgroups and predicting whether a patient subpopulation
might benefit from a particular treatment. Conventional approaches often
evaluate the subgroup treatment effects via parametric modeling and can thus be
susceptible to model mis-specifications. In this manuscript, we take a
model-free semiparametric perspective and aim to efficiently evaluate the
heterogeneous treatment effects of multiple subgroups simultaneously under the
one-step targeted maximum-likelihood estimation (TMLE) framework. When the
number of subgroups is large, we further expand this path of research by
looking at a variation of the one-step TMLE that is robust to the presence of
small estimated propensity scores in finite samples. From our simulations, our
method demonstrates substantial finite sample improvements compared to
conventional methods. In a case study, our method unveils the potential
treatment effect heterogeneity of rs12916-T allele (a proxy for statin usage)
in decreasing Alzheimer's disease risk.
