  In neoadjuvant trials on early-stage breast cancer, patients are usually
randomized into a control group and a treatment group with an additional target
therapy. Early efficacy of the new regimen is assessed via the binary
pathological complete response (pCR) and the eventual efficacy is assessed via
a long-term clinical outcome such as survival. Although pCR is strongly
associated with survival, it has not been confirmed as a surrogate endpoint. To
fully understand its clinical implication, it is important to establish causal
estimands such as the causal effect in survival for patients who would achieve
pCR under the new regimen. Under the principal stratification framework,
previous works focused on sensitivity analyses by varying model parameters in
an imposed model on counterfactual outcomes. Under the same assumptions, we
propose an approach to estimate those model parameters using empirical data and
subsequently the causal estimand of interest. We also extend our approach to
address censored outcome data. The proposed method is applied to a recent
clinical trial and its performance is evaluated via simulation studies.
