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.
翻译:在关于早期乳腺癌的Negadjuvant试验中,病人通常被随机地分成一个控制组和一个有附加目标疗法的治疗组;通过二元病理完整反应(pCR)评估新疗法的早期效果,并通过长期临床结果(如生存)评估最终效果;虽然pcr与生存密切相关,但它没有被确认为替代端点;为了充分理解其临床影响,必须确定因果估计值,如在新疗法下将实现pCR的病人生存的因果影响;在主要分层框架下,以前的工作重点是通过对反事实结果的强制模型的不同模型参数进行敏感度分析;在同样的假设下,我们提出一种办法,利用经验数据来估计这些模型参数,随后是因果估计利息的因果。我们还扩大我们处理受审查的结果数据的方法。拟议方法适用于最近的临床试验,并通过模拟研究来评价其绩效。